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create_FolderTemplates <- function(
path,
mode = "SG",
n_folders = 1,
names = paste("Sample_", 1:n_folders),
verbose = TRUE
){
if(missing(path))
stop("[create_FolderTemplates()] Input for 'path' is missing!", call. = FALSE)
if(!dir.exists(normalizePath(path, mustWork = FALSE)))
stop("[create_FolderTemplates()] ",path, " does not exist!", call. = FALSE)
if(!toupper(mode) %in% c("SG","MG"))
stop("[create_FolderTemplates()] mode = '",mode,"' not supported!", call. = FALSE)
names <-
gsub(
pattern = " ",
replacement = "_",
x = names,
fixed = TRUE
)
names <- rep(names, length.out = n_folders[1])
switch (toupper(mode),
"SG" = extdata <- "extdata/samp1",
"MG" = extdata <- "extdata/FER1"
)
extdata_path <- list.files(
path = system.file(extdata, "", package="BayLum"),
full.names = TRUE,
recursive = TRUE)
if(verbose) cat("\n[create_FolderTemplates()]\n-|\n")
for(i in 1:n_folders[1]){
dir.create(path = paste0(normalizePath(path),"/",names[i]), showWarnings = FALSE)
file.copy(
from = extdata_path,
to = paste0(normalizePath(path),"/",names[i]),
recursive = TRUE,
overwrite = TRUE)
if(verbose) cat(" |__(dir created:)", path,"\n")
}
if(verbose) message("All templates created. Please modify the parameters according to your data!")
} |
`endfunction` <-
function(text) {invisible(NULL)} |
cmorwavf <- function(lb = -8, ub = 8, n = 1000, fb = 5, fc = 1) {
if (!isPosscal(n) || !isWhole(n) || n <= 0)
stop("n must be an integer strictly positive")
if (!isPosscal(fb) || fb <= 0) stop("fb must be a positive scalar > 0")
if (!isPosscal(fc) || fc <= 0) stop("fc must be a positive scalar > 0")
x <- seq(lb, ub, length.out = n)
psi <- ((pi * fb) ^ (-0.5)) * exp(2 * 1i * pi * fc * x) * exp(-x^2 / fb)
list(x = x, psi = psi)
} |
.plotFtest <- function(x,
ftbase=1.01,
siglines=NULL,
xlab="Frequency",
...) {
if(is.null(x$mtm$Ftest) || !("Ftest" %in% class(x))) {
stop(paste("Ftest not computed for given mtm object!"))
}
log <- match.call(expand.dots = )$log
ylab <- match.call(expand.dots = )$ylab
if(is.null(ylab)) ylab <- "Harmonic F-test Statistic"
ylog = "n"
if(is.null(log) || log == "yes") {
ylog = "y"
}
ftestVals = x$mtm$Ftest
ftestVals[ftestVals < ftbase] <- ftbase
ftmax <- max(ftestVals)
.lplotDefault(x$freq, ftestVals, log=ylog, ylab=ylab, xlab=xlab,
ylim=c(ftbase,ftmax), type="l", ...)
if(!is.null(siglines)) {
for(j in 1:length(siglines)) {
if(is.numeric(siglines[j]) && 0.80 <= siglines[j] && 1.000000 >= siglines[j]) {
sig0 <- qf(siglines[j],2,2*x$mtm$k-2)
abline(h=sig0, col="red", lty=2, lwd=1)
mtext(paste(siglines[j]*100,"%",sep=""), side=4, line=0, at=ceiling(sig0), col="red")
}
}
}
}
.lplotSpec <- function(x, ..., dT) {
plot(x, ...)
}
.lplotDefault <- function(x, y, ..., dT) {
plot(x, y, ...)
}
.lftr3p <- function(x, xv, nhi, nehl, nehc, slo, shi) {
ip <- 2
xp <- matrix(NA, nhi, 3)
xp[,3] <- .llftr7(xv, nhi, "hi", "even", "extend", nehl, ip)
xp[,2] <- .llftr7(x, nhi, "-", slo, shi, nehc, ip)
xp[,1] <- xp[,2] - sqrt(xp[,3])
xp[,3] <- xp[,2] + sqrt(xp[,3])
return(xp)
}
.llftr7 <- function(x,nhi,lohi,slo,shi,neh,ip) {
nlo <- 1
y <- array(NA, nhi)
z <- array(NA, nhi+2*neh)
zNlo <- nlo + neh
zNhi <- nhi + neh
zHi <- zNhi + neh
fw <- as.double(neh + 1)
wt <- ((1-((-neh:neh)/fw))*(1+((-neh:neh)/fw)))**ip
cwt <- sum(wt)
wt <- wt/cwt
innerSeq <- zNlo:zNhi
z[innerSeq] <- x
lowerSeq <- 1:neh
z[lowerSeq] <- x[1]
upperSeq <- (zNhi+1):zHi
z[upperSeq] <- x[nhi]
if(tolower(slo) == "even") {
z[rev(lowerSeq)] <- z[(zNlo+1):(zNlo+neh)]
}
if(tolower(slo) == "odd") {
z[rev(lowerSeq)] <- 2.0*z[zNlo] - z[(zNlo+1):(zNlo+neh)]
}
if(tolower(shi) == "even") {
for( j in 1:neh) {
z[zNhi+j] <- z[zNhi-j]
}
}
if(tolower(shi) == "odd") {
for( j in 1:neh) {
z[zNhi + j] = 2.*z[zNhi] - z[zNhi-j]
}
}
if(tolower(lohi) == "hi") {
for( n in (nlo+1):(nhi-1) ) {
if( (x[n] < x[n-1]) && (x[n] < x[n+1]) ) {
zNoff <- n +neh
z[zNoff] <- (x[n-1]+x[n+1])/2.0
}
}
}
if(tolower(lohi) == "lo") {
for( n in (nlo+1):(nhi-1) ) {
if( (x[n] > x[n-1] ) && (x[n] > x[n+1]) ) {
zNoff <- n +neh
z[n] = (x[n-1]+x[n+1])/2.0
}
}
}
zOffSetSeq <- 1:(2*neh+1)
for (n in nlo:nhi) {
y[n] <- sum(wt*z[zOffSetSeq])
zOffSetSeq <- zOffSetSeq +1
}
return(y)
}
.trnrm <- function(k) sqrt(2*k-2)
.C2toF <- function(xx, trnrm_) {
return( trnrm_*log((1.0+sqrt(xx))/(1.0-sqrt(xx)))/2.0 )
}
.FtoMSC <- function(ff, trnrm_) tanh(ff/trnrm_)**2
.paxpt7 <- function(ndata=2000, nmax=40) {
ndec <- round(log10(max(11,ndata)));
nout = 6*ndec -1;
if(nout > nmax) {
return();
}
n <- 0;
out <- array(NA, nout)
for(m in seq(-ndec, -1, 1)) {
for(k in c(1,2,5)) {
n <- n +1;
v <- as.double(k*10**m);
out[n] <- v;
out[nout +1 -n] <- as.double(1.0 - v);
}
}
return(list(out=out, Qnorm=qnorm(out),nout=nout));
}
.cdfToMSqCoh <- function(cdf, k) {
fnavm <- as.double(k-1);
return(1.0 - (1.0 - cdf)**(1.0/fnavm));
}
.mscToCDFquantiles <- function(msc, k) {
1 - (1-msc)^(k-1)
} |
library(rgdal)
library(spdep)
library(rgeos)
library(measurements)
area <- function(spatobj = NULL, folder = NULL, shape = NULL) {
if (is.null(spatobj))
spatobj <- rgdal::readOGR(dsn = folder, layer = shape)
provi <- slot(spatobj, "polygons")
area <- sapply(provi, slot, "area")
return(area)
}
contig <- function(spatobj = NULL, folder = NULL, shape = NULL, queen = FALSE) {
if (is.null(spatobj))
spatobj <- rgdal::readOGR(dsn = folder, layer = shape)
data_ngb <- spdep::poly2nb(spatobj, queen = queen)
contig <- spdep::nb2mat(data_ngb, style = "B", zero.policy = TRUE)
return(contig)
}
perimeter <- function(spatobj = NULL, folder = NULL, shape = NULL) {
if (is.null(spatobj))
spatobj <- rgdal::readOGR(dsn = folder, layer = shape)
perim <- vector(length = length(spatobj))
for (i in 1:length(spatobj)) perim[i] <- rgeos::gLength(spatobj[i, ])
return(perim)
}
distance <- function(spatobj = NULL, folder = NULL, shape = NULL, distin = "m", distout = "m", diagval = "0") {
if (is.null(spatobj))
spatobj <- rgdal::readOGR(dsn = folder, layer = shape)
dist <- matrix(0, nrow = length(spatobj), ncol = length(spatobj))
centroids <- vector("list", length(spatobj))
for (i in 1:length(spatobj)) centroids[[i]] <- rgeos::gCentroid(spatobj[i, ])
for (i in 1:(length(spatobj) - 1)) for (j in (i + 1):length(spatobj)) dist[i, j] <- rgeos::gDistance(centroids[[i]], centroids[[j]])
dist <- dist + t(dist)
if (diagval == "a") {
a <- area(spatobj = spatobj, folder = folder, shape = shape)
diag(dist) <- sqrt(a) * 0.6
}
dist <- measurements::conv_unit(dist, from = distin, to = distout)
return(dist)
}
distcenter <- function(spatobj = NULL, folder = NULL, shape = NULL, center = 1, distin = "m", distout = "m") {
if (is.null(spatobj))
spatobj <- rgdal::readOGR(dsn = folder, layer = shape)
distcenter <- vector(length = length(spatobj))
centroids <- vector("list", length(spatobj))
for (i in 1:length(spatobj)) centroids[[i]] <- rgeos::gCentroid(spatobj[i, ])
for (i in 1:length(spatobj)) distcenter[i] <- rgeos::gDistance(centroids[[i]], centroids[[center]])
distcenter <- measurements::conv_unit(distcenter, from = distin, to = distout)
return(distcenter)
}
boundaries <- function(spatobj = NULL, folder = NULL, shape = NULL) {
if (is.null(spatobj))
spatobj <- rgdal::readOGR(dsn = folder, layer = shape)
boundaries <- contig(spatobj)
mat <- upper.tri(boundaries, diag = FALSE) * 1
boundaries <- boundaries * mat
for (i in 1:(length(spatobj) - 1)) for (j in (i + 1):length(spatobj)) {
if (boundaries[i, j] != 0)
boundaries[i, j] <- rgeos::gLength(rgeos::gIntersection(spatobj[i, ], spatobj[j, ]))
}
boundaries <- boundaries + t(boundaries)
return(boundaries)
} |
computeMu = function(X, Y, optargs=list())
{
if (!is.matrix(X) || !is.numeric(X) || any(is.na(X)))
stop("X: real matrix, no NA")
n = nrow(X)
d = ncol(X)
if (!is.numeric(Y) || length(Y)!=n || any(Y!=0 & Y!=1))
stop("Y: vector of 0 and 1, size nrow(X), no NA")
if (!is.list(optargs))
stop("optargs: list")
M = if (is.null(optargs$M)) computeMoments(X,Y) else optargs$M
K = optargs$K
if (is.null(K))
{
Sigma = svd(M[[2]])$d
large_ratio <- ( abs(Sigma[-d] / Sigma[-1]) > 3 )
K <- if (any(large_ratio)) max(2, which.min(large_ratio)) else d
}
d = ncol(X)
fixed_design = FALSE
jd_nvects = ifelse(!is.null(optargs$jd_nvects), optargs$jd_nvects, 0)
if (jd_nvects == 0)
{
jd_nvects = d
fixed_design = TRUE
}
M2_t = array(dim=c(d,d,jd_nvects))
for (i in seq_len(jd_nvects))
{
rho = if (fixed_design) c(rep(0,i-1),1,rep(0,d-i)) else normalize( rnorm(d) )
M2_t[,,i] = .T_I_I_w(M[[3]],rho)
}
jd_method = ifelse(!is.null(optargs$jd_method), optargs$jd_method, "uwedge")
V =
if (jd_nvects > 1) {
suppressWarnings({jd = jointDiag::ajd(M2_t, method=jd_method)})
if (jd_method=="uwedge") jd$B else MASS::ginv(jd$A)
}
else
eigen(M2_t[,,1])$vectors
M2_t = array(dim=c(d,d,K))
for (i in seq_len(K))
M2_t[,,i] = .T_I_I_w(M[[3]],V[,i])
suppressWarnings({jd = jointDiag::ajd(M2_t, method=jd_method)})
U = if (jd_method=="uwedge") MASS::ginv(jd$B) else jd$A
mu = normalize(U[,1:K])
C = MASS::ginv(mu) %*% M[[1]]
mu[,C < 0] = - mu[,C < 0]
mu
} |
psdVal <- function(species="List",units=c("mm","cm","in"),incl.zero=TRUE,
addLens=NULL,addNames=NULL,showJustSource=FALSE) {
units <- match.arg(units)
PSDlit <- get(utils::data("PSDlit",envir=environment()),envir=environment())
if (iPSDLitCheck(PSDlit,species <- capFirst(species))) {
if (showJustSource) {
PSDlit[PSDlit$species==species,c(1,12)]
} else {
ifelse(units=="in",cols <- 2:6,cols <- 7:11)
PSDvec <- as.matrix(PSDlit[PSDlit$species==species,cols])[1,]
if (units=="mm") PSDvec <- PSDvec*10
names(PSDvec) <- c("stock","quality","preferred","memorable","trophy")
if (incl.zero) {
PSDvec <- c(0,PSDvec)
names(PSDvec)[1] <- "substock"
}
if (!is.null(addLens)) {
addLens <- iHndlAddNames(addLens,addNames)
tmp <- which(PSDvec %in% addLens)
if (length(tmp>0)) {
WARN("At least one Gabelhouse length that was in 'addLens' has been removed.")
PSDvec <- PSDvec[-tmp]
}
PSDvec <- c(PSDvec,addLens)
PSDvec <- PSDvec[order(PSDvec)]
}
PSDvec
}
}
}
iPSDLitCheck <- function(data,species) {
OK <- FALSE
if (length(species)!=1) STOP("'species' can have only one name.")
else if (species=="List") iListSpecies(data)
else if (!any(unique(data$species)==species))
STOP("The Gabelhouse lengths do not exist for ",species,
".\n Type psdVal() for a list of available species.\n\n")
else OK <- TRUE
OK
}
iHndlAddNames <- function(addLens,addNames) {
if (is.null(names(addLens))) {
if (is.null(addNames)) names(addLens) <- as.character(addLens)
else {
if (length(addLens)!=length(addNames))
STOP("'addLens' and 'addNames' have different lengths.")
names(addLens) <- addNames
}
}
addLens
} |
phase.plot <- function(x, y, phases,
arrow.len = min(par()$pin[2] / 30, par()$pin[1] / 40),
arrow.col = "black",
arrow.lwd = arrow.len * 0.3) {
a.row <- seq(1, NROW(phases), round(NROW(phases) / 30))
a.col <- seq(1, NCOL(phases), round(NCOL(phases) / 40))
phases[-a.row, ] <- NA
phases[, -a.col] <- NA
for (i in seq_len(NROW(phases))) {
for (j in seq_len(NCOL(phases))) {
if (!is.na(phases[i, j])) {
arrow(x[j], y[i], l = arrow.len, w = arrow.lwd,
alpha = phases[i, j], col = arrow.col)
}
}
}
}
arrow <- function(x, y, l = 0.1, w = 0.3 * l, alpha, col = "black") {
l2 <- l / 3
w2 <- w / 6
l3 <- l / 2
x1 <- l * cos(alpha)
y1 <- l * sin(alpha)
x2 <- w * cos(alpha + pi / 2)
y2 <- w * sin(alpha + pi / 2)
x7 <- w * cos(alpha + 3 * pi / 2)
y7 <- w * sin(alpha + 3 * pi / 2)
x3 <- l2 * cos(alpha) + w2 * cos(alpha + pi / 2)
y3 <- l2 * sin(alpha) + w2 * sin(alpha + pi / 2)
x6 <- l2 * cos(alpha) + w2 * cos(alpha + 3 * pi / 2)
y6 <- l2 * sin(alpha) + w2 * sin(alpha + 3 * pi / 2)
x4 <- l3 * cos(alpha + pi) + w2 * cos(alpha + pi / 2)
y4 <- l3 * sin(alpha + pi) + w2 * sin(alpha + pi / 2)
x5 <- l3 * cos(alpha + pi) + w2 * cos(alpha + 3 * pi / 2)
y5 <- l3 * sin(alpha + pi) + w2 * sin(alpha + 3 * pi / 2)
X <- (par()$usr[2] - par()$usr[1]) / par()$pin[1] * c(x1,x2,x3,x4,x5,x6,x7)
Y <- (par()$usr[4] - par()$usr[3]) / par()$pin[2] * c(y1,y2,y3,y4,y5,y6,y7)
polygon(x + X, y + Y, col = col, ljoin = 1, border = NA)
}
arrow2 <- function(x, y, angle, size = .1, col = "black",
chr = intToUtf8(0x279B)) {
text(x,y, labels = chr, col = col, cex = 10 * size, srt = 57.29578 * angle)
} |
context("Calculate XMR Upper Long Run Recalculation")
library(testthat)
library(dplyr)
library(tidyr)
Measure <- c(58, 57, 69, 62, 66, 58, 66, 62,
61, 61, 67, 68, 69, 70, 69, 68, 67, 69)
Time <- c(2000:2017)
example_data <- data.frame(Time, Measure)
df <- xmr(example_data, measure = "Measure", recalc = T)
test_that("Upper shortrun recalculation is correct", {
mv <- df$`Moving Range`[df$Order %in% c(11:15)] %>% mean()
avm <- df$`Average Moving Range`[11]
calc <- avm - mv
max <- max(calc, na.rm = T)
expect_lt(max, 0.01)
m <- df$Measure[df$Order %in% c(11:15)] %>% mean()
cl <- df$`Central Line`[11]
calc <- m - cl
max <- max(calc, na.rm = T)
expect_lt(max, 0.01)
})
test_that("Upper Process Limit calculation is correct", {
up <- df$`Upper Natural Process Limit`
calc <- (df$`Central Line` + (2.66 * df$`Average Moving Range`))
max <- max(up - calc, na.rm = T)
expect_lt(max, 0.01)
})
test_that("Lower Process Limit calculation is correct", {
lower <- df$`Lower Natural Process Limit`
calc <- (df$`Central Line` - (2.66 * df$`Average Moving Range`))
calc <- ifelse(calc < 0, 0, calc)
max <- max(lower - calc, na.rm = T)
expect_lt(max, 0.01)
}) |
context("Resource configs")
test_that("User config works",
{
user <- user_config("username", sshkey="random key")
expect_is(user, "user_config")
expect_identical(user$key, "random key")
user <- user_config("username", password="random password")
expect_is(user, "user_config")
expect_identical(user$pwd, "random password")
user <- user_config("username", sshkey="../testthat.R")
expect_is(user, "user_config")
expect_identical(user$key, readLines("../testthat.R"))
})
test_that("Datadisk config works",
{
disk <- datadisk_config(100)
expect_is(disk, "datadisk_config")
expect_identical(disk$res_spec$diskSizeGB, 100)
expect_identical(disk$vm_spec$createOption, "attach")
expect_identical(disk$vm_spec$caching, "None")
expect_null(disk$vm_spec$storageAccountType)
})
test_that("Image config works",
{
expect_error(image_config())
img <- image_config(publisher="pubname", offer="offname", sku="skuname")
expect_is(img, "image_marketplace")
img <- image_config(id="resource_id")
expect_is(img, "image_custom")
})
test_that("Network security group config works",
{
nsg <- nsg_config()
expect_is(nsg, "nsg_config")
res <- build_resource_fields(nsg)
expect_identical(res$properties,
list(securityRules=list())
)
nsg <- nsg_config(list(nsg_rule_allow_ssh))
expect_is(nsg, "nsg_config")
expect_is(nsg$properties$securityRules[[1]], "nsg_rule")
expect_identical(nsg$properties$securityRules[[1]]$name, "Allow-ssh")
res <- build_resource_fields(nsg)
rule <- unclass(nsg_rule_allow_ssh)
rule$properties$priority <- 1010
expect_identical(res$properties,
list(securityRules=list(rule))
)
})
test_that("Public IP address config works",
{
ip <- ip_config()
expect_is(ip, "ip_config")
expect_null(ip$type)
expect_null(ip$dynamic)
ip <- ip_config("static", FALSE)
expect_is(ip, "ip_config")
res <- build_resource_fields(ip)
expect_identical(res$properties,
list(
publicIPAllocationMethod="static",
publicIPAddressVersion="IPv4",
dnsSettings=list(domainNameLabel="[parameters('vmName')]")
)
)
expect_identical(res$sku,
list(name="static")
)
})
test_that("Virtual network config works",
{
vnet <- vnet_config()
expect_is(vnet, "vnet_config")
expect_is(vnet$properties$subnets[[1]], "subnet_config")
res <- build_resource_fields(vnet)
expect_identical(res$properties,
list(
addressSpace=list(addressPrefixes=I("10.0.0.0/16")),
subnets=list(
list(
name="subnet",
properties=list(
addressPrefix="10.0.0.0/16",
networkSecurityGroup=list(id="[variables('nsgId')]")
)
)
)
)
)
vnet <- vnet_config("10.1.0.0/16")
expect_identical(vnet$properties$subnets[[1]]$properties$addressPrefix, "10.1.0.0/16")
vnet <- vnet_config(
address_space="10.1.0.0/16",
subnets=list(subnet_config("mysubnet", addresses="10.0.0.0/24"))
)
expect_identical(vnet$properties$subnets[[1]]$properties$addressPrefix, "10.1.0.0/24")
})
test_that("Network interface config works",
{
nic <- nic_config()
expect_is(nic, "nic_config")
res <- build_resource_fields(nic)
expect_identical(res$properties,
list(
ipConfigurations=list(
list(
name="ipconfig",
properties=list(
privateIPAllocationMethod="dynamic",
subnet=list(id="[variables('subnetId')]"),
publicIPAddress=list(id="[variables('ipId')]")
)
)
)
)
)
})
test_that("Load balancer config works",
{
lb <- lb_config()
expect_is(lb, "lb_config")
expect_null(lb$type)
lb <- lb_config(type="basic")
res <- build_resource_fields(lb)
expect_identical(res$properties,
list(
frontendIPConfigurations=list(
list(
name="[variables('lbFrontendName')]",
properties=list(
publicIPAddress=list(id="[variables('ipId')]")
)
)
),
backendAddressPools=list(
list(
name="[variables('lbBackendName')]"
)
),
loadBalancingRules=list(),
probes=list()
)
)
expect_identical(res$sku,
list(name="basic")
)
lb <- lb_config(type="basic", rules=list(lb_rule_ssh), probes=list(lb_probe_ssh))
expect_is(lb$rules[[1]], "lb_rule")
expect_is(lb$probes[[1]], "lb_probe")
res <- build_resource_fields(lb)
expect_identical(res$properties$loadBalancingRules[[1]], unclass(lb_rule_ssh))
expect_identical(res$properties$probes[[1]], unclass(lb_probe_ssh))
})
test_that("Autoscaler config works",
{
as <- autoscaler_config()
expect_is(as, "as_config")
expect_is(as$properties$profiles[[1]], "as_profile_config")
res <- build_resource_fields(as)
expect_identical(res$properties,
list(
name="[variables('asName')]",
targetResourceUri="[variables('vmId')]",
enabled=TRUE,
profiles=list(
unclass(autoscaler_profile())
)
)
)
}) |
.proctime00 <- proc.time()
library(utils)
options(digits = 5)
options(show.signif.stars = FALSE)
SweaveTeX <- function(file, ...) {
if(!file.exists(file))
stop("File", file, "does not exist in", getwd())
texF <- sub("\\.[RSrs]nw$", ".tex", file)
Sweave(file, ...)
if(!file.exists(texF))
stop("File", texF, "does not exist in", getwd())
readLines(texF)
}
p0 <- paste0
latexEnv <- function(lines, name) {
stopifnot(is.character(lines), is.character(name),
length(lines) >= 2, length(name) == 1)
beg <- p0("\\begin{",name,"}")
end <- p0("\\end{",name,"}")
i <- grep(beg, lines, fixed=TRUE)
j <- grep(end, lines, fixed=TRUE)
if((n <- length(i)) != length(j))
stop(sprintf("different number of %s / %s", beg,end))
if(any(j-1 < i+1))
stop(sprintf("positionally mismatched %s / %s", beg,end))
lapply(mapply(seq, i+1,j-1, SIMPLIFY=FALSE),
function(ind) lines[ind])
}
t1 <- SweaveTeX("swv-keepSrc-1.Rnw")
if(FALSE)
writeLines(t1)
inp <- latexEnv(t1, "Sinput")
out <- latexEnv(t1, "Soutput")
stopifnot(length(inp) == 5,
grepl("
length(out) == 1,
any(grepl("\\includegraphics", t1)))
t2 <- SweaveTeX("keepsource.Rnw")
comml <- grep("
stopifnot(length(comml) == 2,
grepl("initial comment line", comml[1]),
grepl("last comment", comml[2]))
Sweave("customgraphics.Rnw")
Sweave(f <- "Sexpr-verb-ex.Rnw")
tools::texi2pdf(sub("Rnw$","tex", f))
cat('Time elapsed: ', proc.time() - .proctime00,'\n') |
zPath <- function(viv,
cutoff = NULL,
method = c("greedy.weighted", "strictly.weighted"),
connect = TRUE) {
if (!(requireNamespace("zenplots", quietly = TRUE))) {
message("Please install package zenplots to use this function. Note zenplots requires packge graph from Bioconductor, see help for zpath for instructions.")
return(invisible(NULL))
}
method <- match.arg(method)
zpath <- NULL
diag(viv) <- NA
viv[upper.tri(viv)] <- NA
if (!is.numeric(cutoff)) cutoff <- quantile(viv, .8, na.rm = TRUE)
viv[is.na(viv)] <- -Inf
w <- viv > cutoff
if (sum(w) == 0) stop("No off diagonal entries in 'viv' exceed 'cutoff'.")
zinfo <- cbind(viv[w], row(viv)[w], col(viv)[w])
if (nrow(zinfo) == 1) return(rownames(viv)[zinfo[1,2:3]])
if (method == "greedy.weighted") {
zpath <- tryCatch(zpath <- zenplots::zenpath(zinfo[, 1], pairs = zinfo[, -1], method = "greedy.weighted"),
error = function(e) NULL, warning = function(w) {}
)
}
if (method == "strictly.weighted" | is.null(zpath) | length(zpath) == 0) {
zpath <- zenplots::zenpath(zinfo[, 1], pairs = zinfo[, -1], method = "strictly.weighted")
zpath <- zenplots::connect_pairs(zpath)
if (connect) zpath <- unlist(zpath)
}
if (is.numeric(zpath)) {
zpath <- rownames(viv)[zpath]
} else if (is.list(zpath)) zpath <- lapply(zpath, function(z) rownames(viv)[z])
zpath
} |
plotSNHT = function(data, stat, time = NULL, alpha = NULL){
stopifnot(is.numeric(data))
stopifnot(is(stat, "data.frame"))
stopifnot("score" %in% colnames(stat))
if(!is.null(alpha))
stopifnot(is.numeric(alpha))
if(is.null(time)){
time = 1:length(data)
stopifnot(nrow(stat) == length(data))
if("time" %in% colnames(stat))
stop("The snht statistic wasn't computed on evenly spaced data. ",
"Please supply the times to this plotting function.")
stat$time = time
} else {
stopifnot("time" %in% colnames(stat))
time = as.numeric(time)
if(any(is.na(time)))
stop("Supplied times were coerced to numeric, and the result has ",
"missing values. Please check your time vector.")
}
pData = qplot(x = time, y = data)
pStat = qplot(x = stat$time, y = stat$score, geom = "line") +
labs(x = "time", y = "SNHT Statistic")
if(!is.null(alpha))
pStat = pStat + geom_hline(yintercept = qchisq(1-alpha, df = 1),
linetype = 4, color = "blue")
pStat = pStat + geom_vline(xintercept = stat$time[which.max(stat$score)],
color = "red", linetype = 4)
pData = pData + geom_vline(xintercept = stat$time[which.max(stat$score)],
color = "red", linetype = 4)
print(gridExtra::grid.arrange(pData, pStat))
} |
context("BLUP estimates of QTL effects")
calc_blup <-
function(probs, pheno, kinship=NULL, addcovar=NULL, tol=1e-12,
quiet=TRUE)
{
addcovar <- cbind(rep(1, length(pheno)), addcovar)
if(!is.null(kinship)) {
Ke <- decomp_kinship(kinship)
Keval <- Ke$values
Kevec <- Ke$vectors
pheno <- Kevec %*% pheno
addcovar <- Kevec %*% addcovar
probs <- Kevec %*% probs
lmmfit <- Rcpp_fitLMM(Keval, pheno, addcovar, tol=tol)
hsq <- lmmfit$hsq
if(!quiet) message("hsq_pg: ", hsq)
wts <- 1/sqrt(hsq * Keval + (1-hsq))
pheno <- wts * pheno
addcovar <- wts * addcovar
probs <- wts * probs
}
k <- probs %*% t(probs)
ke <- decomp_kinship(k)
keval <- ke$values
kevec <- ke$vectors
pheno <- kevec %*% pheno
addcovar <- kevec %*% addcovar
lmmfit <- Rcpp_fitLMM(keval, pheno, addcovar, tol=tol)
beta <- lmmfit$beta
hsq <- lmmfit$hsq
if(!quiet) message("hsq_qtl: ", hsq)
wts <- hsq * keval + (1-hsq)
u <- as.numeric( t(kevec %*% probs) %*% diag(hsq/wts) %*% (pheno - addcovar %*% beta) )
c(u, beta)
}
iron <- read_cross2(system.file("extdata", "iron.zip", package="qtl2"))
iron <- iron[c(1:30, 190:210),]
phe <- iron$pheno[,1,drop=FALSE]
test_that("scan1blup works with no kinship matrix", {
pr <- calc_genoprob(iron[,"16"])
blup <- scan1blup(pr, phe)
blup_se <- scan1blup(pr, phe, se=TRUE)
expect_equivalent(blup, blup_se)
for(i in 1:dim(pr[[1]])[[3]]) {
blup_alt <- calc_blup(pr[[1]][,,i], phe)
names(blup_alt) <- colnames(blup)
expect_equal(unclass(blup)[i,], blup_alt, tol=1e-6)
}
sex <- as.numeric(iron$covar$sex=="m")
names(sex) <- rownames(iron$covar)
blup <- scan1blup(pr, phe, addcovar=sex)
blup_se <- scan1blup(pr, phe, addcovar=sex, se=TRUE)
expect_equivalent(blup, blup_se)
for(i in 1:dim(pr[[1]])[[3]]) {
blup_alt <- calc_blup(pr[[1]][,,i], phe, addcovar=sex)
names(blup_alt) <- colnames(blup)
expect_equal(unclass(blup)[i,], blup_alt, tolerance=1e-5)
}
})
test_that("scan1blup works with kinship matrix", {
skip_on_cran()
pr <- calc_genoprob(iron)
K <- calc_kinship(pr[,c(1:15,17:19,"X")])
pr <- pr[,"16"]
sex <- as.numeric(iron$covar$sex=="m")
names(sex) <- rownames(iron$covar)
blup <- scan1blup(pr, phe, K, sex)
blup_se <- scan1blup(pr, phe, K, sex, se=TRUE)
expect_equivalent(blup, blup_se)
for(i in 1:dim(pr[[1]])[[3]]) {
blup_alt <- calc_blup(pr[[1]][,,i], phe, K, sex)
names(blup_alt) <- colnames(blup)
expect_equal(unclass(blup)[i,], blup_alt, tolerance=1e-5)
}
})
test_that("scan1blup works with kinship matrix on another chromosome", {
skip_if(isnt_karl(), "this test only run locally")
pr <- calc_genoprob(iron)
K <- calc_kinship(pr[,c(1:10,12:19,"X")])
pr <- pr[,"11"]
sex <- as.numeric(iron$covar$sex=="m")
names(sex) <- rownames(iron$covar)
blup <- scan1blup(pr, phe, K, sex)
blup_se <- scan1blup(pr, phe, K, sex, se=TRUE)
expect_equivalent(blup, blup_se)
for(i in 1:dim(pr[[1]])[[3]]) {
blup_alt <- calc_blup(pr[[1]][,,i], phe, K, sex)
names(blup_alt) <- colnames(blup)
expect_equal(unclass(blup)[i,], blup_alt, tolerance=1e-5)
}
})
test_that("scan1blup deals with mismatching individuals", {
skip_if(isnt_karl(), "this test only run locally")
iron <- read_cross2(system.file("extdata", "iron.zip", package="qtl2"))
map <- insert_pseudomarkers(iron$gmap, step=2.5)
probs <- calc_genoprob(iron, map, error_prob=0.002)
kinship <- calc_kinship(probs, "loco")[["3"]]
probs <- probs[,"3"]
Xc <- get_x_covar(iron)
X <- match(iron$covar$sex, c("f", "m"))-1
names(X) <- rownames(iron$covar)
phe <- iron$pheno[,2,drop=FALSE]
ind <- c(1:50, 101:150)
expected <- scan1blup(probs[ind,], phe[ind,,drop=FALSE], kinship[ind,ind], addcovar=X[ind])
expect_equal(scan1blup(probs[ind,], phe, kinship, addcovar=X), expected)
expect_equal(scan1blup(probs, phe[ind,,drop=FALSE], kinship, addcovar=X), expected)
expect_equal(scan1blup(probs, phe, kinship[ind,ind], addcovar=X), expected)
expect_equal(scan1blup(probs, phe, kinship, addcovar=X[ind]), expected)
expected <- scan1blup(probs[ind,], phe[ind,,drop=FALSE], addcovar=X[ind])
expect_equal(scan1blup(probs[ind,], phe, addcovar=X), expected)
expect_equal(scan1blup(probs, phe[ind,,drop=FALSE], addcovar=X), expected)
expect_equal(scan1blup(probs, phe, addcovar=X[ind]), expected)
}) |
raterpage <- tabItem(tabName = "rateratio",
h2("Precision of a rate ratio"),
"Enter the proportions of events you expect in the groups. If you intend to use uneven allocation ratios (e.g. 2 allocated to group 1 for each participant allocated to group 2), adjust the allocation ratio accordingly. To estimate the ratio of the upper confidence interval limit to the lower limit from a number of events, enter the number of events in 'Number of events'. To estimate the number of observations required to get a ratio of the upper confidence interval limit to the lower limit of X, enter the ratio in 'Upper-lower ratio'.",
tags$br(),
h4("Please enter the following"),
numericInput("rateratio_rate_exp", "Event rate in the exposed group",
value = NULL, step = .01, min = .01, max = .99),
numericInput("rateratio_rate_control", "Event rate in the control group",
value = NULL, step = .01, min = .01, max = .99),
numericInput("rateratio_r", "Allocation ratio", value = 1, step = .2),
"(N2 / N1)",
h4("Please enter one of the following"),
uiOutput("rateratio_resetable_input"),
actionButton("rateratio_reset_input",
"Reset 'Number of observations' or 'Confidence interval width'"),
h4("Results"),
verbatimTextOutput("rateratio_out"),
tableOutput("rateratio_tab"),
"Code to replicate in R:",
verbatimTextOutput("rateratio_code"),
h4("References"),
"Rothamn KJ, Greenland S (2018) Planning Study Size Based on Precision Rather Than Power.", tags$i("Epidemiology"), "29:599-603", tags$a(href = "https://doi.org/10.1097/EDE.0000000000000876","doi:10.1097/EDE.0000000000000876")
)
rateratio_fn <- function(input, code = FALSE){
if(is.na(input$rateratio_n_exp) & is.na(input$rateratio_ciwidth)) {
cat("Awaiting 'number of observations' or 'confidence interval width'")
} else {
z <- ifelse(is.na(input$rateratio_n_exp),
paste0(", prec.level = ", input$rateratio_ciwidth),
paste0(", n1 = ", input$rateratio_n_exp))
x <- paste0("prec_rateratio(rate1 = ", input$rateratio_rate_exp,
", rate2 = ", input$rateratio_rate_control,
", r = ", input$rateratio_r,
z, ", conf.level = ", input$conflevel,
")")
if(code){
cat(x)
} else {
eval(parse(text = x))
}
}
} |
svykappa<-function(formula, design,...) UseMethod("svykappa",design)
svykappa.default<-function(formula, design,...) {
if (ncol(attr(terms(formula), "factors")) != 2)
stop("kappa is only computed for two variables")
rows <- formula[[2]][[2]]
cols <- formula[[2]][[3]]
df <- model.frame(design)
nrow <- length(unique(df[[as.character(rows)]]))
ncol <- length(unique(df[[as.character(cols)]]))
rnames<-paste(".",letters,"_",sep="")
cnames<-paste(".",LETTERS,"_",sep="")
if (nrow != ncol)
stop("number of categories is different")
probs <- eval(bquote(svymean(~.(rows) + .(cols) + interaction(.(rows),
.(cols)), design, ...)))
nms <- c(rnames[1:nrow], cnames[1:ncol], outer(1:nrow,
1:ncol, function(i, j) paste(rnames[i], cnames[j],
sep = ".")))
names(probs) <- nms
v <- vcov(probs)
dimnames(v) <- list(nms, nms)
attr(probs, "var") <- v
obs <- parse(text = paste(nms[nrow + ncol + 1+ (0:(nrow-1))*(ncol+1)],
collapse = "+"))[[1]]
expect <- parse(text = paste(nms[1:nrow], nms[nrow + 1:ncol],
sep = "*", collapse = "+"))[[1]]
svycontrast(probs, list(kappa = bquote((.(obs) - .(expect))/(1 -
.(expect)))))
}
"names<-.svrepstat"<-function(x, value){
if (is.list(x) && !is.null(x$replicates)){
names(x[[1]])<-value
colnames(x$replicates)<-value
x
} else NextMethod()
} |
NULL
.opsworkscm$associate_node_input <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(ServerName = structure(logical(0), tags = list(type = "string")), NodeName = structure(logical(0), tags = list(type = "string")), EngineAttributes = structure(list(structure(list(Name = structure(logical(0), tags = list(type = "string")), Value = structure(logical(0), tags = list(type = "string", sensitive = TRUE))), tags = list(type = "structure"))), tags = list(type = "list"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.opsworkscm$associate_node_output <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(NodeAssociationStatusToken = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.opsworkscm$create_backup_input <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(ServerName = structure(logical(0), tags = list(type = "string")), Description = structure(logical(0), tags = list(type = "string")), Tags = structure(list(structure(list(Key = structure(logical(0), tags = list(type = "string")), Value = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "list"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.opsworkscm$create_backup_output <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(Backup = structure(list(BackupArn = structure(logical(0), tags = list(type = "string")), BackupId = structure(logical(0), tags = list(type = "string")), BackupType = structure(logical(0), tags = list(type = "string")), CreatedAt = structure(logical(0), tags = list(type = "timestamp")), Description = structure(logical(0), tags = list(type = "string")), Engine = structure(logical(0), tags = list(type = "string")), EngineModel = structure(logical(0), tags = list(type = "string")), EngineVersion = structure(logical(0), tags = list(type = "string")), InstanceProfileArn = structure(logical(0), tags = list(type = "string")), InstanceType = structure(logical(0), tags = list(type = "string")), KeyPair = structure(logical(0), tags = list(type = "string")), PreferredBackupWindow = structure(logical(0), tags = list(type = "string")), PreferredMaintenanceWindow = structure(logical(0), tags = list(type = "string")), S3DataSize = structure(logical(0), tags = list(deprecated = TRUE, type = "integer")), S3DataUrl = structure(logical(0), tags = list(deprecated = TRUE, type = "string")), S3LogUrl = structure(logical(0), tags = list(type = "string")), SecurityGroupIds = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list")), ServerName = structure(logical(0), tags = list(type = "string")), ServiceRoleArn = structure(logical(0), tags = list(type = "string")), Status = structure(logical(0), tags = list(type = "string")), StatusDescription = structure(logical(0), tags = list(type = "string")), SubnetIds = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list")), ToolsVersion = structure(logical(0), tags = list(type = "string")), UserArn = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.opsworkscm$create_server_input <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(AssociatePublicIpAddress = structure(logical(0), tags = list(type = "boolean")), CustomDomain = structure(logical(0), tags = list(type = "string")), CustomCertificate = structure(logical(0), tags = list(type = "string")), CustomPrivateKey = structure(logical(0), tags = list(type = "string", sensitive = TRUE)), DisableAutomatedBackup = structure(logical(0), tags = list(type = "boolean")), Engine = structure(logical(0), tags = list(type = "string")), EngineModel = structure(logical(0), tags = list(type = "string")), EngineVersion = structure(logical(0), tags = list(type = "string")), EngineAttributes = structure(list(structure(list(Name = structure(logical(0), tags = list(type = "string")), Value = structure(logical(0), tags = list(type = "string", sensitive = TRUE))), tags = list(type = "structure"))), tags = list(type = "list")), BackupRetentionCount = structure(logical(0), tags = list(type = "integer")), ServerName = structure(logical(0), tags = list(type = "string")), InstanceProfileArn = structure(logical(0), tags = list(type = "string")), InstanceType = structure(logical(0), tags = list(type = "string")), KeyPair = structure(logical(0), tags = list(type = "string")), PreferredMaintenanceWindow = structure(logical(0), tags = list(type = "string")), PreferredBackupWindow = structure(logical(0), tags = list(type = "string")), SecurityGroupIds = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list")), ServiceRoleArn = structure(logical(0), tags = list(type = "string")), SubnetIds = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list")), Tags = structure(list(structure(list(Key = structure(logical(0), tags = list(type = "string")), Value = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "list")), BackupId = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.opsworkscm$create_server_output <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(Server = structure(list(AssociatePublicIpAddress = structure(logical(0), tags = list(type = "boolean")), BackupRetentionCount = structure(logical(0), tags = list(type = "integer")), ServerName = structure(logical(0), tags = list(type = "string")), CreatedAt = structure(logical(0), tags = list(type = "timestamp")), CloudFormationStackArn = structure(logical(0), tags = list(type = "string")), CustomDomain = structure(logical(0), tags = list(type = "string")), DisableAutomatedBackup = structure(logical(0), tags = list(type = "boolean")), Endpoint = structure(logical(0), tags = list(type = "string")), Engine = structure(logical(0), tags = list(type = "string")), EngineModel = structure(logical(0), tags = list(type = "string")), EngineAttributes = structure(list(structure(list(Name = structure(logical(0), tags = list(type = "string")), Value = structure(logical(0), tags = list(type = "string", sensitive = TRUE))), tags = list(type = "structure"))), tags = list(type = "list")), EngineVersion = structure(logical(0), tags = list(type = "string")), InstanceProfileArn = structure(logical(0), tags = list(type = "string")), InstanceType = structure(logical(0), tags = list(type = "string")), KeyPair = structure(logical(0), tags = list(type = "string")), MaintenanceStatus = structure(logical(0), tags = list(type = "string")), PreferredMaintenanceWindow = structure(logical(0), tags = list(type = "string")), PreferredBackupWindow = structure(logical(0), tags = list(type = "string")), SecurityGroupIds = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list")), ServiceRoleArn = structure(logical(0), tags = list(type = "string")), Status = structure(logical(0), tags = list(type = "string")), StatusReason = structure(logical(0), tags = list(type = "string")), SubnetIds = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list")), ServerArn = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.opsworkscm$delete_backup_input <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(BackupId = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.opsworkscm$delete_backup_output <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(), tags = list(type = "structure"))
return(populate(args, shape))
}
.opsworkscm$delete_server_input <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(ServerName = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.opsworkscm$delete_server_output <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(), tags = list(type = "structure"))
return(populate(args, shape))
}
.opsworkscm$describe_account_attributes_input <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(), tags = list(type = "structure"))
return(populate(args, shape))
}
.opsworkscm$describe_account_attributes_output <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(Attributes = structure(list(structure(list(Name = structure(logical(0), tags = list(type = "string")), Maximum = structure(logical(0), tags = list(type = "integer")), Used = structure(logical(0), tags = list(type = "integer"))), tags = list(type = "structure"))), tags = list(type = "list"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.opsworkscm$describe_backups_input <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(BackupId = structure(logical(0), tags = list(type = "string")), ServerName = structure(logical(0), tags = list(type = "string")), NextToken = structure(logical(0), tags = list(type = "string")), MaxResults = structure(logical(0), tags = list(type = "integer"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.opsworkscm$describe_backups_output <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(Backups = structure(list(structure(list(BackupArn = structure(logical(0), tags = list(type = "string")), BackupId = structure(logical(0), tags = list(type = "string")), BackupType = structure(logical(0), tags = list(type = "string")), CreatedAt = structure(logical(0), tags = list(type = "timestamp")), Description = structure(logical(0), tags = list(type = "string")), Engine = structure(logical(0), tags = list(type = "string")), EngineModel = structure(logical(0), tags = list(type = "string")), EngineVersion = structure(logical(0), tags = list(type = "string")), InstanceProfileArn = structure(logical(0), tags = list(type = "string")), InstanceType = structure(logical(0), tags = list(type = "string")), KeyPair = structure(logical(0), tags = list(type = "string")), PreferredBackupWindow = structure(logical(0), tags = list(type = "string")), PreferredMaintenanceWindow = structure(logical(0), tags = list(type = "string")), S3DataSize = structure(logical(0), tags = list(deprecated = TRUE, type = "integer")), S3DataUrl = structure(logical(0), tags = list(deprecated = TRUE, type = "string")), S3LogUrl = structure(logical(0), tags = list(type = "string")), SecurityGroupIds = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list")), ServerName = structure(logical(0), tags = list(type = "string")), ServiceRoleArn = structure(logical(0), tags = list(type = "string")), Status = structure(logical(0), tags = list(type = "string")), StatusDescription = structure(logical(0), tags = list(type = "string")), SubnetIds = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list")), ToolsVersion = structure(logical(0), tags = list(type = "string")), UserArn = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "list")), NextToken = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.opsworkscm$describe_events_input <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(ServerName = structure(logical(0), tags = list(type = "string")), NextToken = structure(logical(0), tags = list(type = "string")), MaxResults = structure(logical(0), tags = list(type = "integer"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.opsworkscm$describe_events_output <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(ServerEvents = structure(list(structure(list(CreatedAt = structure(logical(0), tags = list(type = "timestamp")), ServerName = structure(logical(0), tags = list(type = "string")), Message = structure(logical(0), tags = list(type = "string")), LogUrl = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "list")), NextToken = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.opsworkscm$describe_node_association_status_input <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(NodeAssociationStatusToken = structure(logical(0), tags = list(type = "string")), ServerName = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.opsworkscm$describe_node_association_status_output <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(NodeAssociationStatus = structure(logical(0), tags = list(type = "string")), EngineAttributes = structure(list(structure(list(Name = structure(logical(0), tags = list(type = "string")), Value = structure(logical(0), tags = list(type = "string", sensitive = TRUE))), tags = list(type = "structure"))), tags = list(type = "list"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.opsworkscm$describe_servers_input <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(ServerName = structure(logical(0), tags = list(type = "string")), NextToken = structure(logical(0), tags = list(type = "string")), MaxResults = structure(logical(0), tags = list(type = "integer"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.opsworkscm$describe_servers_output <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(Servers = structure(list(structure(list(AssociatePublicIpAddress = structure(logical(0), tags = list(type = "boolean")), BackupRetentionCount = structure(logical(0), tags = list(type = "integer")), ServerName = structure(logical(0), tags = list(type = "string")), CreatedAt = structure(logical(0), tags = list(type = "timestamp")), CloudFormationStackArn = structure(logical(0), tags = list(type = "string")), CustomDomain = structure(logical(0), tags = list(type = "string")), DisableAutomatedBackup = structure(logical(0), tags = list(type = "boolean")), Endpoint = structure(logical(0), tags = list(type = "string")), Engine = structure(logical(0), tags = list(type = "string")), EngineModel = structure(logical(0), tags = list(type = "string")), EngineAttributes = structure(list(structure(list(Name = structure(logical(0), tags = list(type = "string")), Value = structure(logical(0), tags = list(type = "string", sensitive = TRUE))), tags = list(type = "structure"))), tags = list(type = "list")), EngineVersion = structure(logical(0), tags = list(type = "string")), InstanceProfileArn = structure(logical(0), tags = list(type = "string")), InstanceType = structure(logical(0), tags = list(type = "string")), KeyPair = structure(logical(0), tags = list(type = "string")), MaintenanceStatus = structure(logical(0), tags = list(type = "string")), PreferredMaintenanceWindow = structure(logical(0), tags = list(type = "string")), PreferredBackupWindow = structure(logical(0), tags = list(type = "string")), SecurityGroupIds = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list")), ServiceRoleArn = structure(logical(0), tags = list(type = "string")), Status = structure(logical(0), tags = list(type = "string")), StatusReason = structure(logical(0), tags = list(type = "string")), SubnetIds = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list")), ServerArn = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "list")), NextToken = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.opsworkscm$disassociate_node_input <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(ServerName = structure(logical(0), tags = list(type = "string")), NodeName = structure(logical(0), tags = list(type = "string")), EngineAttributes = structure(list(structure(list(Name = structure(logical(0), tags = list(type = "string")), Value = structure(logical(0), tags = list(type = "string", sensitive = TRUE))), tags = list(type = "structure"))), tags = list(type = "list"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.opsworkscm$disassociate_node_output <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(NodeAssociationStatusToken = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.opsworkscm$export_server_engine_attribute_input <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(ExportAttributeName = structure(logical(0), tags = list(type = "string")), ServerName = structure(logical(0), tags = list(type = "string")), InputAttributes = structure(list(structure(list(Name = structure(logical(0), tags = list(type = "string")), Value = structure(logical(0), tags = list(type = "string", sensitive = TRUE))), tags = list(type = "structure"))), tags = list(type = "list"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.opsworkscm$export_server_engine_attribute_output <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(EngineAttribute = structure(list(Name = structure(logical(0), tags = list(type = "string")), Value = structure(logical(0), tags = list(type = "string", sensitive = TRUE))), tags = list(type = "structure")), ServerName = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.opsworkscm$list_tags_for_resource_input <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(ResourceArn = structure(logical(0), tags = list(type = "string")), NextToken = structure(logical(0), tags = list(type = "string")), MaxResults = structure(logical(0), tags = list(type = "integer"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.opsworkscm$list_tags_for_resource_output <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(Tags = structure(list(structure(list(Key = structure(logical(0), tags = list(type = "string")), Value = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "list")), NextToken = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.opsworkscm$restore_server_input <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(BackupId = structure(logical(0), tags = list(type = "string")), ServerName = structure(logical(0), tags = list(type = "string")), InstanceType = structure(logical(0), tags = list(type = "string")), KeyPair = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.opsworkscm$restore_server_output <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(), tags = list(type = "structure"))
return(populate(args, shape))
}
.opsworkscm$start_maintenance_input <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(ServerName = structure(logical(0), tags = list(type = "string")), EngineAttributes = structure(list(structure(list(Name = structure(logical(0), tags = list(type = "string")), Value = structure(logical(0), tags = list(type = "string", sensitive = TRUE))), tags = list(type = "structure"))), tags = list(type = "list"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.opsworkscm$start_maintenance_output <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(Server = structure(list(AssociatePublicIpAddress = structure(logical(0), tags = list(type = "boolean")), BackupRetentionCount = structure(logical(0), tags = list(type = "integer")), ServerName = structure(logical(0), tags = list(type = "string")), CreatedAt = structure(logical(0), tags = list(type = "timestamp")), CloudFormationStackArn = structure(logical(0), tags = list(type = "string")), CustomDomain = structure(logical(0), tags = list(type = "string")), DisableAutomatedBackup = structure(logical(0), tags = list(type = "boolean")), Endpoint = structure(logical(0), tags = list(type = "string")), Engine = structure(logical(0), tags = list(type = "string")), EngineModel = structure(logical(0), tags = list(type = "string")), EngineAttributes = structure(list(structure(list(Name = structure(logical(0), tags = list(type = "string")), Value = structure(logical(0), tags = list(type = "string", sensitive = TRUE))), tags = list(type = "structure"))), tags = list(type = "list")), EngineVersion = structure(logical(0), tags = list(type = "string")), InstanceProfileArn = structure(logical(0), tags = list(type = "string")), InstanceType = structure(logical(0), tags = list(type = "string")), KeyPair = structure(logical(0), tags = list(type = "string")), MaintenanceStatus = structure(logical(0), tags = list(type = "string")), PreferredMaintenanceWindow = structure(logical(0), tags = list(type = "string")), PreferredBackupWindow = structure(logical(0), tags = list(type = "string")), SecurityGroupIds = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list")), ServiceRoleArn = structure(logical(0), tags = list(type = "string")), Status = structure(logical(0), tags = list(type = "string")), StatusReason = structure(logical(0), tags = list(type = "string")), SubnetIds = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list")), ServerArn = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.opsworkscm$tag_resource_input <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(ResourceArn = structure(logical(0), tags = list(type = "string")), Tags = structure(list(structure(list(Key = structure(logical(0), tags = list(type = "string")), Value = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "list"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.opsworkscm$tag_resource_output <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(), tags = list(type = "structure"))
return(populate(args, shape))
}
.opsworkscm$untag_resource_input <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(ResourceArn = structure(logical(0), tags = list(type = "string")), TagKeys = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.opsworkscm$untag_resource_output <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(), tags = list(type = "structure"))
return(populate(args, shape))
}
.opsworkscm$update_server_input <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(DisableAutomatedBackup = structure(logical(0), tags = list(type = "boolean")), BackupRetentionCount = structure(logical(0), tags = list(type = "integer")), ServerName = structure(logical(0), tags = list(type = "string")), PreferredMaintenanceWindow = structure(logical(0), tags = list(type = "string")), PreferredBackupWindow = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.opsworkscm$update_server_output <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(Server = structure(list(AssociatePublicIpAddress = structure(logical(0), tags = list(type = "boolean")), BackupRetentionCount = structure(logical(0), tags = list(type = "integer")), ServerName = structure(logical(0), tags = list(type = "string")), CreatedAt = structure(logical(0), tags = list(type = "timestamp")), CloudFormationStackArn = structure(logical(0), tags = list(type = "string")), CustomDomain = structure(logical(0), tags = list(type = "string")), DisableAutomatedBackup = structure(logical(0), tags = list(type = "boolean")), Endpoint = structure(logical(0), tags = list(type = "string")), Engine = structure(logical(0), tags = list(type = "string")), EngineModel = structure(logical(0), tags = list(type = "string")), EngineAttributes = structure(list(structure(list(Name = structure(logical(0), tags = list(type = "string")), Value = structure(logical(0), tags = list(type = "string", sensitive = TRUE))), tags = list(type = "structure"))), tags = list(type = "list")), EngineVersion = structure(logical(0), tags = list(type = "string")), InstanceProfileArn = structure(logical(0), tags = list(type = "string")), InstanceType = structure(logical(0), tags = list(type = "string")), KeyPair = structure(logical(0), tags = list(type = "string")), MaintenanceStatus = structure(logical(0), tags = list(type = "string")), PreferredMaintenanceWindow = structure(logical(0), tags = list(type = "string")), PreferredBackupWindow = structure(logical(0), tags = list(type = "string")), SecurityGroupIds = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list")), ServiceRoleArn = structure(logical(0), tags = list(type = "string")), Status = structure(logical(0), tags = list(type = "string")), StatusReason = structure(logical(0), tags = list(type = "string")), SubnetIds = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list")), ServerArn = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.opsworkscm$update_server_engine_attributes_input <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(ServerName = structure(logical(0), tags = list(type = "string")), AttributeName = structure(logical(0), tags = list(type = "string")), AttributeValue = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.opsworkscm$update_server_engine_attributes_output <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(Server = structure(list(AssociatePublicIpAddress = structure(logical(0), tags = list(type = "boolean")), BackupRetentionCount = structure(logical(0), tags = list(type = "integer")), ServerName = structure(logical(0), tags = list(type = "string")), CreatedAt = structure(logical(0), tags = list(type = "timestamp")), CloudFormationStackArn = structure(logical(0), tags = list(type = "string")), CustomDomain = structure(logical(0), tags = list(type = "string")), DisableAutomatedBackup = structure(logical(0), tags = list(type = "boolean")), Endpoint = structure(logical(0), tags = list(type = "string")), Engine = structure(logical(0), tags = list(type = "string")), EngineModel = structure(logical(0), tags = list(type = "string")), EngineAttributes = structure(list(structure(list(Name = structure(logical(0), tags = list(type = "string")), Value = structure(logical(0), tags = list(type = "string", sensitive = TRUE))), tags = list(type = "structure"))), tags = list(type = "list")), EngineVersion = structure(logical(0), tags = list(type = "string")), InstanceProfileArn = structure(logical(0), tags = list(type = "string")), InstanceType = structure(logical(0), tags = list(type = "string")), KeyPair = structure(logical(0), tags = list(type = "string")), MaintenanceStatus = structure(logical(0), tags = list(type = "string")), PreferredMaintenanceWindow = structure(logical(0), tags = list(type = "string")), PreferredBackupWindow = structure(logical(0), tags = list(type = "string")), SecurityGroupIds = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list")), ServiceRoleArn = structure(logical(0), tags = list(type = "string")), Status = structure(logical(0), tags = list(type = "string")), StatusReason = structure(logical(0), tags = list(type = "string")), SubnetIds = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list")), ServerArn = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "structure"))
return(populate(args, shape))
} |
test_that("r_squared_profiling remains stable", {
cycles <- alloy$cycles
status <- alloy$status
data <- reliability_data(x = cycles, status = status)
tbl_john <- estimate_cdf(data, "johnson")
threshold <- seq(0, min(cycles[status == 1]) - 0.1, length.out = 100)
profile_r2 <- r_squared_profiling.default(
x = tbl_john$x[tbl_john$status == 1],
y = tbl_john$prob[tbl_john$status == 1],
thres = threshold,
distribution = "weibull3"
)
expect_snapshot_output(profile_r2)
})
test_that("rank_regression remains stable", {
obs <- seq(10000, 100000, 10000)
status <- c(0, 1, 1, 0, 0, 0, 1, 0, 1, 0)
data <- reliability_data(x = obs, status = status)
tbl_john <- estimate_cdf(data, "johnson")
rr <- rank_regression(
tbl_john,
distribution = "weibull",
conf_level = .90
)
expect_snapshot_output(rr$coefficients)
expect_snapshot_output(rr$r_squared)
expect_snapshot_output(rr)
cycles <- c(300, 300, 300, 300, 300, 291, 274, 271, 269, 257, 256, 227, 226,
224, 213, 211, 205, 203, 197, 196, 190, 189, 188, 187, 184, 180,
180, 177, 176, 173, 172, 171, 170, 170, 169, 168, 168, 162, 159,
159, 159, 159, 152, 152, 149, 149, 144, 143, 141, 141, 140, 139,
139, 136, 135, 133, 131, 129, 123, 121, 121, 118, 117, 117, 114,
112, 108, 104, 99, 99, 96, 94)
status <- c(rep(0, 5), rep(1, 67))
data <- reliability_data(x = cycles, status = status)
tbl_john <- estimate_cdf(data, "johnson")
rr <- rank_regression(
tbl_john,
distribution = "weibull3",
conf_level = .90
)
expect_snapshot_output(rr$coefficients)
expect_snapshot_output(rr$r_squared)
expect_snapshot_output(rr)
})
test_that("rank_regression supports multiple methods", {
data <- reliability_data(shock, x = "distance", status = "status")
methods <- c("johnson", "nelson", "kaplan")
cdf_tbl <- estimate_cdf(data, methods)
rr <- rank_regression(
x = cdf_tbl,
distribution = "weibull"
)
expect_equal(length(rr), 3)
expect_true(all(methods %in% names(rr)))
}) |
matchValues_varLabels <- function(GADSdat, mc_vars, values, label_by_hand = character(0)) {
check_GADSdat(GADSdat)
if(!is.vector(values) & length(values) > 0) stop("values needs to be a character vector of at least length 1.")
values <- unique(values[!is.na(values)])
labels <- unique(extractMeta(GADSdat, mc_vars)[, c("varName", "varLabel")])
if(!all(label_by_hand %in% mc_vars)) stop("All variable names in label_by_hand must be variables in mc_vars.")
names(values) <- values
matches <- lapply(values, function(value) {
out <- labels[grep(value, labels$varLabel), "varName"]
if(length(out) > 1) stop("Multiple matches found for ", value, ". There must be always exactly 1 match.")
out
})
matches <- unlist(matches[sapply(matches, function(x) length(x) > 0)])
matches <- c(matches, label_by_hand)
unassigned_mcs <- mc_vars[!mc_vars %in% matches]
if(length(unassigned_mcs) > 0) stop("The following mc_vars have not been assigned a value: ", paste(unassigned_mcs, collapse = ", "))
names(mc_vars) <- names(matches)[match(mc_vars, matches)]
mc_vars
} |
testMutationalPatternPair <- function (a, mtx, PRINT=FALSE) {
return (testMutationalPatternBinom (mtx[,a[1]], mtx[,a[2]], PRINT=PRINT))
} |
new_s2_xptr <- function(x = list(), class = character()) {
if (!is.list(x) || is.object(x)) {
stop("x must be a bare list of 'externalptr' objects")
}
class(x) <- union(class, "s2_xptr")
x
}
validate_s2_xptr <- function(x) {
type <- vapply(unclass(x), typeof, character(1))
valid_items <- type %in% c("externalptr", "NULL")
if (any(!valid_items)) {
stop("Items must be externalptr objects or NULL")
}
invisible(x)
}
`[.s2_xptr` <- function(x, i) {
new_s2_xptr(NextMethod(), class(x))
}
`[[.s2_xptr` <- function(x, i) {
x[i]
}
`c.s2_xptr` <- function(...) {
dots <- list(...)
inherits_first <- vapply(dots, inherits, class(dots[[1]])[1], FUN.VALUE = logical(1))
if (!all(inherits_first)) {
stop(sprintf("All items must inherit from '%s'", class(dots[[1]])[1]))
}
xptr <- new_s2_xptr(NextMethod(), class(dots[[1]]))
validate_s2_xptr(xptr)
xptr
}
rep.s2_xptr <- function(x, ...) {
if (length(x) == 0) {
new_s2_xptr(list(), class(x))
} else {
new_s2_xptr(NextMethod(), class(x))
}
}
rep_len.s2_xptr <- function(x, length.out) {
rep(x, length.out = length.out)
}
as.data.frame.s2_xptr <- function(x, ..., optional = FALSE) {
if (!optional) {
NextMethod()
} else {
new_data_frame(list(x))
}
}
str.s2_xptr <- function(object, ..., indent.str = "", width = getOption("width")) {
if (length(object) == 0) {
cat(paste0(" ", class(object)[1], "[0]\n"))
return(invisible(object))
}
width <- width - nchar(indent.str) - 2
length <- min(length(object), ceiling(width / 5))
formatted <- format(object[seq_len(length)], trim = TRUE)
title <- paste0(" ", class(object)[1], "[1:", length(object), "]")
cat(
paste0(
title,
" ",
strtrim(paste0(formatted, collapse = ", "), width - nchar(title)),
"\n"
)
)
invisible(object)
}
print.s2_xptr <- function(x, ...) {
cat(sprintf("<%s[%s]>\n", class(x)[1], length(x)))
if (length(x) == 0) {
return(invisible(x))
}
out <- stats::setNames(format(x, ...), names(x))
print(out, quote = FALSE)
invisible(x)
} |
knitr::knit("vignettes/AfterFitting.Rmd.orig",
output = "vignettes/AfterFitting.Rmd")
if (dir.exists("vignettes/figures_AfterFitting")) {
unlink("vignettes/figures_AfterFitting", recursive = TRUE)
}
file.rename(from = "figures_AfterFitting",
to = "vignettes/figures_AfterFitting")
knitr::knit("vignettes/ModelSpecification.Rmd.orig",
output = "vignettes/ModelSpecification.Rmd")
if (dir.exists("vignettes/figures_ModelSpecification")) {
unlink("vignettes/figures_ModelSpecification", recursive = TRUE)
}
file.rename(from = "figures_ModelSpecification",
to = "vignettes/figures_ModelSpecification")
knitr::knit("vignettes/MinimalExample.Rmd.orig",
output = "vignettes/MinimalExample.Rmd")
if (dir.exists("vignettes/figures_MinimalExample")) {
unlink("vignettes/figures_MinimalExample", recursive = TRUE)
}
file.rename(from = "figures_MinimalExample",
to = "vignettes/figures_MinimalExample")
knitr::knit("vignettes/SelectingParameters.Rmd.orig",
output = "vignettes/SelectingParameters.Rmd")
if (dir.exists("vignettes/figures_SelectingParameters")) {
unlink("vignettes/figures_SelectingParameters", recursive = TRUE)
}
file.rename(from = "figures_SelectingParameters",
to = "vignettes/figures_SelectingParameters") |
collect.disk.frame <- function(x, ..., parallel = !is.null(attr(x,"recordings"))) {
cids = get_chunk_ids(x, full.names = TRUE, strip_extension = FALSE)
if(nchunks(x) > 0) {
if(parallel) {
tmp<-future.apply::future_lapply(cids, function(.x) {
get_chunk.disk.frame(x, .x, full.names = TRUE)
}, future.seed = TRUE)
return(rbindlist(tmp))
} else {
purrr::map_dfr(cids, ~get_chunk.disk.frame(x, .x, full.names = TRUE))
}
} else {
data.table()
}
}
collect_list <- function(x, simplify = FALSE, parallel = !is.null(attr(x,"recordings")), ...) {
cids = get_chunk_ids(x, full.names = TRUE, strip_extension = FALSE)
if(length(cids) > 0) {
list_of_results = NULL
if (parallel) {
list_of_results = future.apply::future_lapply(cids, function(.x) {
get_chunk(x, .x, full.names = TRUE)
}, future.seed=TRUE)
} else {
list_of_results = lapply(cids, function(cid) {
get_chunk(x, cid, full.names = TRUE)
})
}
if (simplify) {
return(simplify2array(list_of_results))
} else {
return(list_of_results)
}
} else {
list()
}
} |
sec2hms <- function (...) {
x <- c(...)
if (any(x > 3600)) stop("`...` must be <= 3600")
hs <- x%%3600
h <- floor(hs)
ms <- (hs - h) * 60
m <- floor(ms)
s <- floor((ms - m) * 60)
hms <- lapply(list(h, m, s), function(x) sprintf("%02d", x))
chron::times(paste(hms[[1]], hms[[2]], hms[[3]], sep=":"))
}
hijack <- function(FUN, ...){
.FUN <- FUN
args <- list(...)
invisible(lapply(seq_along(args), function(i) {
formals(.FUN)[[names(args)[i]]] <<- args[[i]]
}))
.FUN
}
mtabulate <- function (vects) {
lev <- sort(unique(unlist(vects)))
dat <- do.call(rbind, lapply(vects, function(x, lev) {
tabulate(factor(x, levels = lev, ordered = TRUE), nbins = length(lev))
}, lev = lev))
colnames(dat) <- sort(lev)
data.frame(dat, check.names = FALSE)
}
validate_relate <- function(x){
grepl("[\\+\\-\\*\\/]([^_]+)_(.+)", x, perl=TRUE)
} |
draw_app <- function() {
appDir <- system.file("app", package = "rusk")
if (appDir == "") {
stop("Could not find . Try re-installing `rusk`.", call. = FALSE)
}
shiny::runApp(appDir, display.mode = "normal")
} |
x <- list(createTestObj(return.types=TRUE), as.character(1:16))
objSizes <- matrix(0, nrow=length(x[[1]]), ncol=length(x[[2]]), dimnames=x)
objLens <- objSizes
for (s in seq(ncol(objSizes))) {
cat("Doing scale=", s, ": ", sep="")
for (i in seq(nrow(objSizes))) {
cat("", i)
x <- createTestObj(i, s)
objSizes[i, s] <- object.size(x)
objLens[i, s] <- length(x)
}
cat("\n")
}
objSizes
objLens |
netsplit <- function(x, method,
upper = TRUE,
reference.group = x$reference.group,
baseline.reference = x$baseline.reference,
order = NULL,
sep.trts = x$sep.trts, quote.trts = "",
tol.direct = 0.0005,
fixed = x$fixed,
random = x$random,
backtransf = x$backtransf,
warn = FALSE, warn.deprecated = gs("warn.deprecated"),
verbose = FALSE,
...) {
chkclass(x, "netmeta")
x <- updateversion(x)
is.bin <- inherits(x, "netmetabin")
if (!missing(method))
method <- setchar(method, c("Back-calculation", "SIDDE"))
else {
if (is.bin)
method <- "SIDDE"
else
method <- "Back-calculation"
}
chklogical(upper)
chklogical(baseline.reference)
if (!is.null(order)) {
order <- setseq(order, x$trts)
baseline.reference <- FALSE
reference.group <- ""
}
chkchar(sep.trts)
chkchar(quote.trts)
chknumeric(tol.direct, min = 0, length = 1)
if (!is.null(backtransf))
chklogical(backtransf)
chklogical(warn)
chklogical(verbose)
args <- list(...)
chklogical(warn.deprecated)
fixed <- deprecated(fixed, missing(fixed), args, "comb.fixed",
warn.deprecated)
chklogical(fixed)
fixed.logical <- fixed
random <- deprecated(random, missing(random), args, "comb.random",
warn.deprecated)
chklogical(random)
random.logical <- random
seq.comps <- rownames(x$Cov.fixed)
dat.trts <- matrix(unlist(compsplit(seq.comps, x$sep.trts)),
ncol = 2, byrow = TRUE)
dat.trts <- as.data.frame(dat.trts, stringsAsFactors = FALSE)
names(dat.trts) <- c("treat1", "treat2")
if (!upper) {
t1 <- dat.trts$treat1
dat.trts$treat1 <- dat.trts$treat2
dat.trts$treat2 <- t1
}
if (is.null(order)) {
wo <- rep_len(FALSE, length(seq.comps))
if (reference.group != "") {
reference.group <- setref(reference.group, colnames(x$TE.fixed))
if (baseline.reference)
wo <- dat.trts$treat1 == reference.group
else
wo <- dat.trts$treat2 == reference.group
}
else
if (!missing(baseline.reference))
warning("Argument 'baseline.reference' ignored as ",
"reference group is not defined ",
"(argument 'reference.group').")
if (any(wo)) {
t1.wo <- dat.trts$treat1[wo]
dat.trts$treat1[wo] <- dat.trts$treat2[wo]
dat.trts$treat2[wo] <- t1.wo
}
}
else {
treat1.pos <- as.numeric(factor(dat.trts$treat1, levels = order))
treat2.pos <- as.numeric(factor(dat.trts$treat2, levels = order))
wo <- treat1.pos > treat2.pos
if (any(wo)) {
ttreat1 <- dat.trts$treat1
dat.trts$treat1[wo] <- dat.trts$treat2[wo]
dat.trts$treat2[wo] <- ttreat1[wo]
ttreat1.pos <- treat1.pos
treat1.pos[wo] <- treat2.pos[wo]
treat2.pos[wo] <- ttreat1.pos[wo]
}
o <- order(treat1.pos, treat2.pos)
dat.trts <- dat.trts[o, ]
}
comparison <- as.character(interaction(paste(quote.trts, dat.trts$treat1,
quote.trts, sep = ""),
paste(quote.trts, dat.trts$treat2,
quote.trts, sep = ""),
sep = sep.trts))
if (!(is.bin & method == "SIDDE")) {
prop.direct.fixed <- rep_len(NA, length(x$prop.direct.fixed))
seq.comps.fixed <- names(x$prop.direct.fixed)
trts.fixed <-
matrix(unlist(compsplit(seq.comps.fixed, x$sep.trts)),
ncol = 2, byrow = TRUE)
trts.fixed <- as.data.frame(trts.fixed, stringsAsFactors = FALSE)
names(trts.fixed) <- c("treat1", "treat2")
for (i in seq_along(comparison)) {
sel.i <-
(trts.fixed$treat1 == dat.trts$treat1[i] &
trts.fixed$treat2 == dat.trts$treat2[i]) |
(trts.fixed$treat1 == dat.trts$treat2[i] &
trts.fixed$treat2 == dat.trts$treat1[i])
prop.direct.fixed[i] <- x$prop.direct.fixed[sel.i]
}
prop.direct.random <- rep_len(NA, length(x$prop.direct.random))
seq.comps.random <- names(x$prop.direct.random)
trts.random <-
matrix(unlist(compsplit(seq.comps.random, x$sep.trts)),
ncol = 2, byrow = TRUE)
trts.random <- as.data.frame(trts.random, stringsAsFactors = FALSE)
names(trts.random) <- c("treat1", "treat2")
for (i in seq_along(comparison)) {
sel.i <-
(trts.random$treat1 == dat.trts$treat1[i] &
trts.random$treat2 == dat.trts$treat2[i]) |
(trts.random$treat1 == dat.trts$treat2[i] &
trts.random$treat2 == dat.trts$treat1[i])
prop.direct.random[i] <- x$prop.direct.random[sel.i]
}
}
if (method == "Back-calculation") {
sel.one.fixed <- abs(x$P.fixed - 1) < tol.direct
TE.indirect.fixed <- x$TE.indirect.fixed
seTE.indirect.fixed <- x$seTE.indirect.fixed
lower.indirect.fixed <- x$lower.indirect.fixed
upper.indirect.fixed <- x$upper.indirect.fixed
statistic.indirect.fixed <- x$statistic.indirect.fixed
pval.indirect.fixed <- x$pval.indirect.fixed
TE.indirect.fixed[sel.one.fixed] <- NA
seTE.indirect.fixed[sel.one.fixed] <- NA
lower.indirect.fixed[sel.one.fixed] <- NA
upper.indirect.fixed[sel.one.fixed] <- NA
statistic.indirect.fixed[sel.one.fixed] <- NA
pval.indirect.fixed[sel.one.fixed] <- NA
sel.one.random <- abs(x$P.random - 1) < tol.direct
TE.indirect.random <- x$TE.indirect.random
seTE.indirect.random <- x$seTE.indirect.random
lower.indirect.random <- x$lower.indirect.random
upper.indirect.random <- x$upper.indirect.random
statistic.indirect.random <- x$statistic.indirect.random
pval.indirect.random <- x$pval.indirect.random
TE.indirect.random[sel.one.random] <- NA
seTE.indirect.random[sel.one.random] <- NA
lower.indirect.random[sel.one.random] <- NA
upper.indirect.random[sel.one.random] <- NA
statistic.indirect.random[sel.one.random] <- NA
pval.indirect.random[sel.one.random] <- NA
}
is.tictoc <- FALSE
if (method == "SIDDE") {
if (is.null(x$data))
stop("SIDDE method only available for network meta-analysis objects ",
"created with argument 'keepdata' equal to TRUE.")
if (verbose)
cat("Start computations for SIDDE approach\n")
is.tictoc <- is.installed.package("tictoc", stop = FALSE)
dat <- x$data
dat <- dat[order(dat$.studlab, dat$.treat1, dat$.treat2), ]
if (!is.null(dat$.subset))
dat <- dat[dat$.subset, , drop = FALSE]
if (!is.null(dat$.drop))
dat <- dat[!dat$.drop, , drop = FALSE]
idx.d <- which(!is.na(x$TE.direct.fixed), arr.ind = TRUE)
idx.d <- idx.d[idx.d[, 1] < idx.d[, 2], , drop = FALSE]
rownames(idx.d) <- seq_len(nrow(idx.d))
idx1 <- idx.d[, 1]
idx2 <- idx.d[, 2]
n.comps <- nrow(idx.d)
trts <- x$trts
TE.indirect.fixed <- x$TE.direct.fixed
TE.indirect.fixed[!is.na(TE.indirect.fixed)] <- NA
seTE.indirect.fixed <- TE.indirect.fixed
TE.indirect.random <- x$TE.direct.random
TE.indirect.random[!is.na(TE.indirect.random)] <- NA
seTE.indirect.random <- TE.indirect.random
if (is.tictoc) {
tictoc <- rep(NA, n.comps)
names.tictoc <- ""
}
for (i in seq_len(n.comps)) {
idx1.i <- idx1[i]
idx2.i <- idx2[i]
if (is.tictoc)
tictoc::tic()
if (verbose)
cat(paste0("- ",
paste(trts[idx1.i], trts[idx2.i], sep = sep.trts),
" (", i, "/", n.comps, ")\n"))
drop.i <-
(dat$.treat1 == trts[idx1.i] & dat$.treat2 == trts[idx2.i]) |
(dat$.treat2 == trts[idx1.i] & dat$.treat1 == trts[idx2.i])
drop.studies <- unique(dat$.studlab[drop.i])
dat.i <- dat[!(dat$.studlab %in% drop.studies), , drop = FALSE]
dat.i$.design <- NULL
if (nrow(dat.i) > 0)
con <- netconnection(dat.i$.treat1, dat.i$.treat2, dat.i$.studlab)
else
con <- list(n.subnets = 0)
if (con$n.subnets == 1) {
if (is.bin)
net.i <- netmetabin(dat.i$.event1, dat.i$.n1,
dat.i$.event2, dat.i$.n2,
dat.i$.treat1, dat.i$.treat2,
dat.i$.studlab,
data = dat.i,
sm = x$sm, method = x$method,
fixed = fixed.logical,
random = random.logical,
warn = warn)
else
net.i <- netmeta(dat.i$.TE, dat.i$.seTE,
dat.i$.treat1, dat.i$.treat2,
dat.i$.studlab,
data = dat.i,
fixed = fixed.logical,
random = random.logical,
warn = warn)
if (trts[idx1.i] %in% rownames(net.i$TE.fixed) &
trts[idx2.i] %in% colnames(net.i$TE.fixed)) {
TE.indirect.fixed[idx1.i, idx2.i] <-
net.i$TE.fixed[trts[idx1.i], trts[idx2.i]]
TE.indirect.fixed[idx2.i, idx1.i] <-
net.i$TE.fixed[trts[idx2.i], trts[idx1.i]]
seTE.indirect.fixed[idx1.i, idx2.i] <-
seTE.indirect.fixed[idx2.i, idx1.i] <-
net.i$seTE.fixed[trts[idx1.i], trts[idx2.i]]
}
if (!is.bin) {
if (trts[idx1.i] %in% rownames(net.i$TE.random) &
trts[idx2.i] %in% colnames(net.i$TE.random)) {
TE.indirect.random[idx1.i, idx2.i] <-
net.i$TE.random[trts[idx1.i], trts[idx2.i]]
TE.indirect.random[idx2.i, idx1.i] <-
net.i$TE.random[trts[idx2.i], trts[idx1.i]]
seTE.indirect.random[idx1.i, idx2.i] <-
seTE.indirect.random[idx2.i, idx1.i] <-
net.i$seTE.random[trts[idx1.i], trts[idx2.i]]
}
}
}
if (is.tictoc) {
tictoc.i <- tictoc::toc(func.toc = NULL)
tictoc[i] <- as.numeric(tictoc.i$toc) - as.numeric(tictoc.i$tic)
names.tictoc[i] <- paste(trts[idx1.i], trts[idx2.i], sep = sep.trts)
if (verbose)
cat(paste(round(tictoc[i], 3), "sec elapsed\n"))
}
}
ci.if <- ci(TE.indirect.fixed, seTE.indirect.fixed, x$level.ma)
lower.indirect.fixed <- ci.if$lower
upper.indirect.fixed <- ci.if$upper
statistic.indirect.fixed <- ci.if$statistic
pval.indirect.fixed <- ci.if$p
if (!is.bin) {
ci.ir <- ci(TE.indirect.random, seTE.indirect.random, x$level.ma)
lower.indirect.random <- ci.ir$lower
upper.indirect.random <- ci.ir$upper
statistic.indirect.random <- ci.ir$statistic
pval.indirect.random <- ci.ir$p
}
}
TE.direct.fixed <- x$TE.direct.fixed
seTE.direct.fixed <- x$seTE.direct.fixed
lower.direct.fixed <- x$lower.direct.fixed
upper.direct.fixed <- x$upper.direct.fixed
statistic.direct.fixed <- x$statistic.direct.fixed
pval.direct.fixed <- x$pval.direct.fixed
if (!is.null(x$P.fixed)) {
sel.zero.fixed <- abs(x$P.fixed) < tol.direct
TE.direct.fixed[sel.zero.fixed] <- NA
seTE.direct.fixed[sel.zero.fixed] <- NA
lower.direct.fixed[sel.zero.fixed] <- NA
upper.direct.fixed[sel.zero.fixed] <- NA
statistic.direct.fixed[sel.zero.fixed] <- NA
pval.direct.fixed[sel.zero.fixed] <- NA
}
TE.direct.random <- x$TE.direct.random
seTE.direct.random <- x$seTE.direct.random
lower.direct.random <- x$lower.direct.random
upper.direct.random <- x$upper.direct.random
statistic.direct.random <- x$statistic.direct.random
pval.direct.random <- x$pval.direct.random
if (!is.null(x$P.random)) {
sel.zero.random <- abs(x$P.random) < tol.direct
TE.direct.random[sel.zero.random] <- NA
seTE.direct.random[sel.zero.random] <- NA
lower.direct.random[sel.zero.random] <- NA
upper.direct.random[sel.zero.random] <- NA
statistic.direct.random[sel.zero.random] <- NA
pval.direct.random[sel.zero.random] <- NA
}
fixed <- direct.fixed <- indirect.fixed <-
data.frame(comparison,
TE = NA, seTE = NA, lower = NA, upper = NA,
statistic = NA, p = NA,
stringsAsFactors = FALSE)
direct.fixed$I2 <- direct.fixed$tau <- direct.fixed$tau2 <-
direct.fixed$Q <- NA
k <- rep_len(NA, length(comparison))
for (i in seq_along(comparison)) {
t1.i <- dat.trts$treat1[i]
t2.i <- dat.trts$treat2[i]
fixed$TE[i] <- x$TE.fixed[t1.i, t2.i]
fixed$seTE[i] <- x$seTE.fixed[t1.i, t2.i]
fixed$lower[i] <- x$lower.fixed[t1.i, t2.i]
fixed$upper[i] <- x$upper.fixed[t1.i, t2.i]
fixed$statistic[i] <- x$statistic.fixed[t1.i, t2.i]
fixed$p[i] <- x$pval.fixed[t1.i, t2.i]
k[i] <- x$A.matrix[t1.i, t2.i]
direct.fixed$TE[i] <- TE.direct.fixed[t1.i, t2.i]
direct.fixed$seTE[i] <- seTE.direct.fixed[t1.i, t2.i]
direct.fixed$lower[i] <- lower.direct.fixed[t1.i, t2.i]
direct.fixed$upper[i] <- upper.direct.fixed[t1.i, t2.i]
direct.fixed$statistic[i] <- statistic.direct.fixed[t1.i, t2.i]
direct.fixed$p[i] <- pval.direct.fixed[t1.i, t2.i]
direct.fixed$Q[i] <- x$Q.direct[t1.i, t2.i]
direct.fixed$tau2[i] <- x$tau2.direct[t1.i, t2.i]
direct.fixed$tau[i] <- x$tau.direct[t1.i, t2.i]
direct.fixed$I2[i] <- x$I2.direct[t1.i, t2.i]
indirect.fixed$TE[i] <- TE.indirect.fixed[t1.i, t2.i]
indirect.fixed$seTE[i] <- seTE.indirect.fixed[t1.i, t2.i]
indirect.fixed$lower[i] <- lower.indirect.fixed[t1.i, t2.i]
indirect.fixed$upper[i] <- upper.indirect.fixed[t1.i, t2.i]
indirect.fixed$statistic[i] <- statistic.indirect.fixed[t1.i, t2.i]
indirect.fixed$p[i] <- pval.indirect.fixed[t1.i, t2.i]
}
m.fixed <-
suppressWarnings(metagen(direct.fixed$TE - indirect.fixed$TE,
sqrt(direct.fixed$seTE^2 +
indirect.fixed$seTE^2),
level = x$level.ma,
method.tau = "DL", method.tau.ci = ""))
compare.fixed <- data.frame(comparison,
TE = m.fixed$TE,
seTE = m.fixed$seTE,
lower = m.fixed$lower,
upper = m.fixed$upper,
statistic = m.fixed$statistic,
p = m.fixed$pval,
z = m.fixed$statistic,
stringsAsFactors = FALSE)
sel.k0 <- k == 0
vars <- c("TE", "seTE", "lower", "upper", "statistic", "p")
indirect.fixed[sel.k0, vars] <- fixed[sel.k0, vars]
if (!is.bin) {
random <- direct.random <- indirect.random <-
data.frame(comparison,
TE = NA, seTE = NA, lower = NA, upper = NA,
statistic = NA, p = NA,
stringsAsFactors = FALSE)
predict <- data.frame(comparison, lower = NA, upper = NA,
stringsAsFactors = FALSE)
direct.random$I2 <- direct.random$tau <- direct.random$tau2 <-
direct.random$Q <- NA
for (i in seq_along(comparison)) {
t1.i <- dat.trts$treat1[i]
t2.i <- dat.trts$treat2[i]
random$TE[i] <- x$TE.random[t1.i, t2.i]
random$seTE[i] <- x$seTE.random[t1.i, t2.i]
random$lower[i] <- x$lower.random[t1.i, t2.i]
random$upper[i] <- x$upper.random[t1.i, t2.i]
random$statistic[i] <- x$statistic.random[t1.i, t2.i]
random$p[i] <- x$pval.random[t1.i, t2.i]
direct.random$TE[i] <- TE.direct.random[t1.i, t2.i]
direct.random$seTE[i] <- seTE.direct.random[t1.i, t2.i]
direct.random$lower[i] <- lower.direct.random[t1.i, t2.i]
direct.random$upper[i] <- upper.direct.random[t1.i, t2.i]
direct.random$statistic[i] <- statistic.direct.random[t1.i, t2.i]
direct.random$p[i] <- pval.direct.random[t1.i, t2.i]
direct.random$Q[i] <- x$Q.direct[t1.i, t2.i]
direct.random$tau2[i] <- x$tau2.direct[t1.i, t2.i]
direct.random$tau[i] <- x$tau.direct[t1.i, t2.i]
direct.random$I2[i] <- x$I2.direct[t1.i, t2.i]
indirect.random$TE[i] <- TE.indirect.random[t1.i, t2.i]
indirect.random$seTE[i] <- seTE.indirect.random[t1.i, t2.i]
indirect.random$lower[i] <- lower.indirect.random[t1.i, t2.i]
indirect.random$upper[i] <- upper.indirect.random[t1.i, t2.i]
indirect.random$statistic[i] <- statistic.indirect.random[t1.i, t2.i]
indirect.random$p[i] <- pval.indirect.random[t1.i, t2.i]
predict$lower[i] <- x$lower.predict[t1.i, t2.i]
predict$upper[i] <- x$upper.predict[t1.i, t2.i]
}
m.random <-
suppressWarnings(metagen(direct.random$TE - indirect.random$TE,
sqrt(direct.random$seTE^2 +
indirect.random$seTE^2),
level = x$level.ma,
method.tau = "DL", method.tau.ci = ""))
compare.random <- data.frame(comparison,
TE = m.random$TE,
seTE = m.random$seTE,
lower = m.random$lower,
upper = m.random$upper,
statistic = m.random$statistic,
p = m.random$pval,
z = m.random$statistic,
stringsAsFactors = FALSE)
indirect.random[sel.k0, vars] <- random[sel.k0, vars]
}
else {
random <- fixed
random[!is.na(random)] <- NA
random$comparison <- comparison
direct.random <- indirect.random <- compare.random <- random
predict <- random[, c("comparison", "lower", "upper")]
}
x$fixed <- fixed.logical
x$random <- random.logical
res <- list(comparison = comparison,
k = k,
prop.fixed =
if (is.bin & method == "SIDDE") NULL
else prop.direct.fixed,
fixed = fixed,
direct.fixed = direct.fixed,
indirect.fixed = indirect.fixed,
compare.fixed = compare.fixed,
prop.random =
if (is.bin & method == "SIDDE") NULL
else prop.direct.random,
random = random,
direct.random = direct.random,
indirect.random = indirect.random,
compare.random = compare.random,
predict = predict,
method = method,
sm = x$sm,
level.ma = x$level.ma,
prediction = x$prediction,
level.predict = x$level.predict,
tau = x$tau,
reference.group = reference.group,
baseline.reference = baseline.reference,
order = order,
sep.trts = sep.trts,
quote.trts = quote.trts,
nchar.trts = x$nchar.trts,
tol.direct = tol.direct,
backtransf = backtransf,
x = x,
version = packageDescription("netmeta")$Version
)
if (is.tictoc) {
res$tictoc <- tictoc
names(res$tictoc) <- names.tictoc
}
class(res) <- c("netsplit",
if (is.bin & method == "SIDDE") "netsplit.netmetabin")
res
}
print.netsplit <- function(x,
fixed = x$x$fixed,
random = x$x$random,
show = "all",
overall = TRUE,
ci = FALSE,
test = show %in% c("all", "with.direct", "both"),
only.reference = FALSE,
sortvar = NULL,
nchar.trts = x$nchar.trts,
digits = gs("digits"),
digits.stat = gs("digits.stat"),
digits.pval = gs("digits.pval"),
digits.prop = max(gs("digits.pval") - 2, 2),
text.NA = ".",
backtransf = x$backtransf,
scientific.pval = gs("scientific.pval"),
big.mark = gs("big.mark"),
legend = TRUE,
warn.deprecated = gs("warn.deprecated"),
...) {
chkclass(x, "netsplit")
x <- updateversion(x)
is.bin <- inherits(x, "netsplit.netmetabin")
oldopts <- options(width = 200)
on.exit(options(oldopts))
chklogical(overall)
chklogical(ci)
chklogical(test)
missing.only.reference <- missing(only.reference)
if (!missing.only.reference)
chklogical(only.reference)
mf <- match.call()
error <- try(sortvar.x <- eval(mf[[match("sortvar", names(mf))]],
x,
enclos = sys.frame(sys.parent())),
silent = TRUE)
if (!any(class(error) == "try-error"))
sortvar <- sortvar.x
if (!is.null(sortvar)) {
if (length(dim(sortvar)) == 2) {
if (dim(sortvar)[1] != length(x$comparison))
stop("Argument 'sortvar' must be of length ",
length(x$comparison), ".",
call. = FALSE)
if (is.numeric(sortvar)) {
sortvar[is.zero(abs(sortvar), n = 1000)] <- 0
sortvar[is.zero(1 - abs(sortvar), n = 1000)] <-
1 * sign(sortvar)[is.zero(1 - abs(sortvar), n = 1000)]
}
sortvar <- order(do.call(order, as.list(as.data.frame(sortvar))))
}
else
chklength(sortvar, length(x$comparison),
text = paste0("Argument 'sortvar' must be of length ",
length(x$comparison), "."))
if (!is.numeric(sortvar))
sortvar <- setchar(sortvar, x$comparison)
}
if (is.null(nchar.trts))
nchar.trts <- 666
chknumeric(nchar.trts, length = 1)
chknumeric(digits, min = 0, length = 1)
chknumeric(digits.stat, min = 0, length = 1)
chknumeric(digits.pval, min = 1, length = 1)
chknumeric(digits.prop, min = 0, length = 1)
if (is.null(backtransf))
backtransf <- TRUE
chklogical(backtransf)
chklogical(scientific.pval)
chklogical(legend)
fun <- "print.netmeta"
args <- list(...)
chklogical(warn.deprecated)
fixed <- deprecated(fixed, missing(fixed), args, "comb.fixed",
warn.deprecated)
chklogical(fixed)
fixed.logical <- fixed
random <- deprecated(random, missing(random), args, "comb.random",
warn.deprecated)
chklogical(random)
random.logical <- random
show <-
deprecated(show, missing(show), args, "showall")
if (is.logical(show))
if (show)
show <- "all"
else
show <- "both"
show <- setchar(show, c("all", "both", "with.direct",
"direct.only", "indirect.only",
"reference.only"))
if (show == "reference.only") {
warning("Argument 'show = \"reference.only\" replaced with ",
"'only.reference = TRUE'.",
call. = FALSE)
show <- "both"
if (missing.only.reference)
only.reference <- TRUE
}
sm <- x$sm
sm.lab <- sm
relative <- is.relative.effect(sm)
if (!backtransf & relative)
sm.lab <- paste("log", sm, sep = "")
if (!(sm.lab == "" | sm.lab == "log"))
sm.lab <- paste("(", sm.lab, ") ", sep = "")
else
sm.lab <- ""
level.ma <- x$level.ma
ci.lab <- paste(100 * level.ma, "%-CI", sep ="")
random.available <- !is.null(x$random)
if (!random.available & random) {
warning("No results for random effects model available. ",
"Argument 'random' set to FALSE.",
call. = FALSE)
random <- FALSE
}
if (show == "all")
sel <- rep_len(TRUE, length(x$direct.fixed$TE))
else if (show == "with.direct")
sel <- !is.na(x$direct.fixed$TE)
else if (show == "both")
sel <- !is.na(x$direct.fixed$TE) & !is.na(x$indirect.fixed$TE)
else if (show == "direct.only")
sel <- !is.na(x$direct.fixed$TE) & is.na(x$indirect.fixed$TE)
else if (show == "indirect.only")
sel <- is.na(x$direct.fixed$TE) & !is.na(x$fixed$TE)
if (only.reference) {
if (x$reference.group == "") {
warning("First treatment used as reference as argument ",
"'reference.group' was unspecified in netsplit().",
call. = FALSE)
x$reference.group <-
compsplit(x$comparison, x$sep.trts)[[1]][1]
}
sel.ref <-
apply(!is.na(sapply(compsplit(x$comparison, x$sep.trts),
match, x$reference.group)), 2, sum) >= 1
sel <- sel & sel.ref
}
comp <- x$comparison[sel]
k <- x$k[sel]
prop.fixed <- x$prop.fixed[sel]
TE.fixed <- x$fixed$TE[sel]
lower.fixed <- x$fixed$lower[sel]
upper.fixed <- x$fixed$upper[sel]
TE.direct.fixed <- x$direct.fixed$TE[sel]
lower.direct.fixed <- x$direct.fixed$lower[sel]
upper.direct.fixed <- x$direct.fixed$upper[sel]
TE.indirect.fixed <- x$indirect.fixed$TE[sel]
lower.indirect.fixed <- x$indirect.fixed$lower[sel]
upper.indirect.fixed <- x$indirect.fixed$upper[sel]
TE.compare.fixed <- x$compare.fixed$TE[sel]
lower.compare.fixed <- x$compare.fixed$lower[sel]
upper.compare.fixed <- x$compare.fixed$upper[sel]
statistic.compare.fixed <- x$compare.fixed$statistic[sel]
pval.compare.fixed <- x$compare.fixed$p[sel]
if (random.available) {
prop.random <- x$prop.random[sel]
TE.random <- x$random$TE[sel]
lower.random <- x$random$lower[sel]
upper.random <- x$random$upper[sel]
TE.direct.random <- x$direct.random$TE[sel]
lower.direct.random <- x$direct.random$lower[sel]
upper.direct.random <- x$direct.random$upper[sel]
TE.indirect.random <- x$indirect.random$TE[sel]
lower.indirect.random <- x$indirect.random$lower[sel]
upper.indirect.random <- x$indirect.random$upper[sel]
TE.compare.random <- x$compare.random$TE[sel]
lower.compare.random <- x$compare.random$lower[sel]
upper.compare.random <- x$compare.random$upper[sel]
statistic.compare.random <- x$compare.random$statistic[sel]
pval.compare.random <- x$compare.random$p[sel]
}
if (backtransf & relative) {
TE.fixed <- exp(TE.fixed)
lower.fixed <- exp(lower.fixed)
upper.fixed <- exp(upper.fixed)
TE.direct.fixed <- exp(TE.direct.fixed)
lower.direct.fixed <- exp(lower.direct.fixed)
upper.direct.fixed <- exp(upper.direct.fixed)
TE.indirect.fixed <- exp(TE.indirect.fixed)
lower.indirect.fixed <- exp(lower.indirect.fixed)
upper.indirect.fixed <- exp(upper.indirect.fixed)
TE.compare.fixed <- exp(TE.compare.fixed)
lower.compare.fixed <- exp(lower.compare.fixed)
upper.compare.fixed <- exp(upper.compare.fixed)
if (random.available) {
TE.random <- exp(TE.random)
lower.random <- exp(lower.random)
upper.random <- exp(upper.random)
TE.direct.random <- exp(TE.direct.random)
lower.direct.random <- exp(lower.direct.random)
upper.direct.random <- exp(upper.direct.random)
TE.indirect.random <- exp(TE.indirect.random)
lower.indirect.random <- exp(lower.indirect.random)
upper.indirect.random <- exp(upper.indirect.random)
TE.compare.random <- exp(TE.compare.random)
lower.compare.random <- exp(lower.compare.random)
upper.compare.random <- exp(upper.compare.random)
}
}
fixed <- list(comp = comp,
k = k,
prop = formatPT(prop.fixed, digits = digits.prop))
names.fixed <- c("comparison", "k", "prop")
if (overall) {
fixed$TE.fixed <- formatN(TE.fixed, digits, text.NA = text.NA,
big.mark = big.mark)
names.fixed <- c(names.fixed, "nma")
if (ci) {
fixed$ci.fixed <- formatCI(round(lower.fixed, digits),
round(upper.fixed, digits))
fixed$ci.fixed[is.na(fixed$ci.fixed)] <- text.NA
names.fixed <- c(names.fixed, ci.lab)
}
}
fixed$TE.direct.fixed <- formatN(TE.direct.fixed, digits, text.NA = text.NA,
big.mark = big.mark)
names.fixed <- c(names.fixed, "direct")
if (ci) {
fixed$ci.direct.fixed <- formatCI(round(lower.direct.fixed, digits),
round(upper.direct.fixed, digits))
fixed$ci.direct.fixed[is.na(fixed$ci.direct.fixed)] <- text.NA
names.fixed <- c(names.fixed, ci.lab)
}
fixed$TE.indirect.fixed <- formatN(TE.indirect.fixed, digits,
text.NA = text.NA, big.mark = big.mark)
names.fixed <- c(names.fixed, "indir.")
if (ci) {
fixed$ci.indirect.fixed <- formatCI(round(lower.indirect.fixed, digits),
round(upper.indirect.fixed, digits))
fixed$ci.indirect.fixed[is.na(fixed$ci.indirect.fixed)] <- text.NA
names.fixed <- c(names.fixed, ci.lab)
}
if (test) {
fixed$diff <- formatN(TE.compare.fixed, digits, text.NA = text.NA,
big.mark = big.mark)
names.fixed <- c(names.fixed, if (backtransf & relative) "RoR" else "Diff")
if (ci) {
fixed$ci.diff <- formatCI(round(lower.compare.fixed, digits),
round(upper.compare.fixed, digits))
fixed$ci.diff[is.na(fixed$ci.diff)] <- text.NA
names.fixed <- c(names.fixed, ci.lab)
}
fixed$statistic <- formatN(statistic.compare.fixed, digits.stat,
big.mark = big.mark)
fixed$statistic[fixed$statistic == "--"] <- text.NA
fixed$p <- formatPT(pval.compare.fixed, digits = digits.pval,
scientific = scientific.pval)
fixed$p[rmSpace(fixed$p) == "--"] <- text.NA
names.fixed <- c(names.fixed, c("z", "p-value"))
}
fixed <- as.data.frame(fixed)
names(fixed) <- names.fixed
if (random.available) {
random.logical <- random
random <- list(comp = comp,
k = k,
prop = formatPT(prop.random, digits = digits.prop))
names.random <- c("comparison", "k", "prop")
if (overall) {
random$TE.random <- formatN(TE.random, digits, text.NA = text.NA,
big.mark = big.mark)
names.random <- c(names.random, "nma")
if (ci) {
random$ci.random <- formatCI(round(lower.random, digits),
round(upper.random, digits))
random$ci.random[is.na(random$ci.random)] <- text.NA
names.random <- c(names.random, ci.lab)
}
}
random$TE.direct.random <- formatN(TE.direct.random, digits,
text.NA = text.NA,
big.mark = big.mark)
names.random <- c(names.random, "direct")
if (ci) {
random$ci.direct.random <- formatCI(round(lower.direct.random, digits),
round(upper.direct.random, digits))
random$ci.direct.random[is.na(random$ci.direct.random)] <- text.NA
names.random <- c(names.random, ci.lab)
}
random$TE.indirect.random <- formatN(TE.indirect.random, digits,
text.NA = text.NA,
big.mark = big.mark)
names.random <- c(names.random, "indir.")
if (ci) {
random$ci.indirect.random <- formatCI(round(lower.indirect.random, digits),
round(upper.indirect.random, digits))
random$ci.indirect.random[is.na(random$ci.indirect.random)] <- text.NA
names.random <- c(names.random, ci.lab)
}
if (test) {
random$diff <- formatN(TE.compare.random, digits, text.NA = text.NA,
big.mark = big.mark)
names.random <- c(names.random, if (backtransf & relative) "RoR" else "Diff")
if (ci) {
random$ci.diff <- formatCI(round(lower.compare.random, digits),
round(upper.compare.random, digits))
random$ci.diff[is.na(random$ci.diff)] <- text.NA
names.random <- c(names.random, ci.lab)
}
random$statistic <- formatN(statistic.compare.random, digits.stat,
big.mark = big.mark)
random$statistic[random$statistic == "--"] <- text.NA
random$p <- formatPT(pval.compare.random, digits = digits.pval,
scientific = scientific.pval)
random$p[rmSpace(random$p) == "--"] <- text.NA
names.random <- c(names.random, c("z", "p-value"))
}
random <- as.data.frame(random)
names(random) <- names.random
}
noprop <- is.bin | x$method == "SIDDE" | all(fixed$prop == "")
if (noprop) {
fixed <- fixed[, !(names(fixed) %in% "prop")]
if (random.available)
random <- random[, !(names(random) %in% "prop")]
}
if (!is.null(sortvar)) {
sortvar <- sortvar[sel]
o <- order(sortvar)
if (fixed.logical)
fixed <- fixed[o, ]
if (random.logical)
random <- random[o, ]
}
if (fixed.logical | random.logical) {
if (x$method == "SIDDE")
cat("Separate indirect from direct design evidence (SIDDE)\n\n")
else
cat(paste("Separate indirect from direct evidence (SIDE)",
"using back-calculation method\n\n"))
}
else
legend <- FALSE
if (fixed.logical) {
cat("Fixed effects model: \n\n")
fixed[is.na(fixed)] <- text.NA
trts <- unique(sort(unlist(compsplit(fixed$comparison, x$sep.trts))))
fixed$comparison <- comps(fixed$comparison, trts, x$sep.trts, nchar.trts)
prmatrix(fixed, quote = FALSE, right = TRUE,
rowlab = rep("", dim(fixed)[1]))
if (random.logical)
cat("\n")
}
if (random.logical) {
cat("Random effects model: \n\n")
random[is.na(random)] <- text.NA
trts <- unique(sort(unlist(compsplit(random$comparison, x$sep.trts))))
random$comparison <- comps(random$comparison, trts, x$sep.trts, nchar.trts)
prmatrix(random, quote = FALSE, right = TRUE,
rowlab = rep("", dim(random)[1]))
}
if (legend) {
cat("\nLegend:\n")
cat(" comparison - Treatment comparison\n")
cat(" k - Number of studies providing direct evidence\n")
if (!noprop)
cat(" prop - Direct evidence proportion\n")
if (overall)
cat(paste(" nma - Estimated treatment effect ", sm.lab,
"in network meta-analysis\n", sep = ""))
cat(paste(" direct - Estimated treatment effect ", sm.lab,
"derived from direct evidence\n", sep = ""))
cat(paste(" indir. - Estimated treatment effect ", sm.lab,
"derived from indirect evidence\n", sep = ""))
if (test) {
if (backtransf & relative)
cat(" RoR - Ratio of Ratios (direct versus indirect)\n")
else
cat(" Diff - Difference between direct and indirect treatment estimates\n")
cat(" z - z-value of test for disagreement (direct versus indirect)\n")
cat(" p-value - p-value of test for disagreement (direct versus indirect)\n")
}
trts.abbr <- treats(trts, nchar.trts)
diff.trts <- trts != trts.abbr
if (any(diff.trts)) {
cat("\n")
tmat <- data.frame(trts.abbr, trts)
names(tmat) <- c("Abbreviation", "Treatment name")
tmat <- tmat[diff.trts, ]
tmat <- tmat[order(tmat$Abbreviation), ]
prmatrix(tmat, quote = FALSE, right = TRUE,
rowlab = rep("", length(trts.abbr)))
}
}
invisible(NULL)
} |
fitAdaptRandom2 <- function(outcomes,freq,nclass=2,initoutcomep,initclassp,initlambdacoef,initltaucoef,
level2size,constload,calcSE=FALSE,justEM,gh,probit,byclass,qniterations,
penalty,EMtol,verbose=FALSE) {
outcomes <- as.matrix(outcomes)
mode(outcomes) <- "integer"
momentdata <- replicate(nclass, NULL)
nlevel1 <- level2size
nlevel2 <- dim(outcomes)[2]/level2size
nlevel3 <- length(freq)
if (constload) nlambda <- 1
else nlambda <- nlevel1
outcomestart <- nclass
outcomeend <- (nclass+nlevel1*nlevel2*nclass-1)
if (byclass) {
lambdastart <- (nlevel1*nlevel2*nclass+nclass)
lambdaend <- (nlevel1*nlevel1*nclass+nclass+nclass*nlambda-1)
taustart <- (nlevel1*nlevel2*nclass+nclass+nclass*nlambda)
tauend <- (nlevel1*nlevel2*nclass+nclass+nclass*nlambda+nclass-1)
} else {
lambdastart <- (nlevel1*nlevel2*nclass+nclass)
lambdaend <- (nlevel1*nlevel2*nclass+nclass+nlambda-1)
taustart <- (nlevel1*nlevel2*nclass+nclass+nlambda)
tauend <- (nlevel1*nlevel2*nclass+nclass+nlambda)
}
calclikelihood <- function(classx,outcomex,lambdacoef,ltaucoef,
updatemoments=FALSE,calcfitted=FALSE,zprop=NULL) {
classp2 <- c(0,classx)
classp2 <- exp(classp2)/sum(exp(classp2))
newmoments <- replicate(nclass, NULL)
ill <- matrix(rep(NA,nclass*nlevel3),ncol=nclass)
mylambdacoef <- lambdacoef
if (constload) {
if (byclass) mylambdacoef <- matrix(rep(lambdacoef,nlevel1),nrow=nclass)
else mylambdacoef <- rep(lambdacoef,nlevel1)
}
for (iclass in 1:nclass) {
result <- .Call("bernoulliprobrandom2",outcomes,outcomex[iclass,],
if (byclass) mylambdacoef[iclass,] else mylambdacoef,
if (byclass) ltaucoef[iclass] else ltaucoef,
gh,momentdata[[iclass]],
probit,updatemoments,level2size)
ill[,iclass] <- exp(result[[1]])
if (updatemoments) newmoments[[iclass]] <- result[[2]]
}
if (is.null(zprop)) {
for (iclass in 1:nclass) ill[,iclass] <- ill[,iclass]*classp2[iclass]
ill2 <- log(rowSums(ill))
ll <- sum(ill2*freq)
} else {
ill2 <- rowSums(log(ill)*zprop)
ll <- sum(ill2*freq)
}
if (probit) {
outcomep <- pnorm(as.vector(outcomex))
noutcomep <- pnorm(as.vector(-outcomex))
} else {
outcomep <- as.vector(1/(1+exp(-outcomex)))
noutcomep <- as.vector(1/(1+exp(outcomex)))
}
penll <- ll+penalty/(nclass*2)*sum(log(outcomep))+penalty/(nclass*2)*sum(log(noutcomep))
if (is.nan(penll) || is.infinite(penll)) penll <- -1.0*.Machine$double.xmax
if (calcfitted) {
fitted <- exp(ill2)*sum(ifelse(apply(outcomes,1,function(x)
any(is.na(x))),0,freq))*
ifelse(apply(outcomes,1,function(x) any(is.na(x))),NA,1)
classprob <- ill/exp(ill2)
return(list(logLik=ll,penlogLik=penll,moments=newmoments,fitted=fitted,classprob=classprob))
} else return(list(logLik=ll,penlogLik=penll,moments=newmoments))
}
adaptivefit <- function(classx,outcomex,lambdacoef,ltaucoef) {
fitparams <- function(classx,outcomex,lambdacoef,ltaucoef,
calcSE,noiterations=qniterations,zprop=NULL) {
calcllfornlm <- function(params,zprop) {
oneiteration <- calclikelihood(if (nclass==1) NULL else params[1:(nclass-1)],
matrix(params[outcomestart:outcomeend],nrow=nclass),
if (byclass) matrix(params[lambdastart:lambdaend],nrow=nclass) else params[lambdastart:lambdaend],
params[taustart:tauend],
zprop=zprop)
return(-oneiteration$penlogLik)
}
nlm1 <- nlm(calcllfornlm, c(classx,as.vector(outcomex),lambdacoef,ltaucoef),
iterlim = noiterations,
print.level=ifelse(verbose,2,0),
check.analyticals = FALSE,hessian=calcSE,zprop=zprop)
return(list(penlogLik=-nlm1$minimum,
classx=if (nclass==1) NULL else nlm1$estimate[1:(nclass-1)],
outcomex=matrix(nlm1$estimate[outcomestart:outcomeend],nrow=nclass),
lambdacoef= if (byclass) matrix(nlm1$estimate[lambdastart:lambdaend],nrow=nclass) else nlm1$estimate[lambdastart:lambdaend],
ltaucoef=nlm1$estimate[taustart:tauend],
nlm=nlm1))
}
oneiteration <- calclikelihood(classx, outcomex, lambdacoef,ltaucoef,
updatemoments=TRUE)
currll <- oneiteration$penlogLik
if (verbose) cat('Initial ll',currll,"\n")
lastll <- 2*currll
while (abs((lastll-currll)/lastll)>1.0e-6) {
lastll <- currll
momentdata <<- oneiteration$moments
oneiteration <- calclikelihood(classx,outcomex,lambdacoef,ltaucoef,
updatemoments=TRUE)
currll <- oneiteration$penlogLik
zprop <- oneiteration$classprob
if (verbose) cat("current ll",currll,"\n")
}
adaptive <- TRUE
prevll <- -Inf
nadaptive <- 0
while(adaptive) {
fitresults <- fitparams(classx,outcomex,lambdacoef,ltaucoef,
calcSE=FALSE,zprop=zprop)
currll <- fitresults$penlogLik
outcomex <- fitresults$outcomex
classx <- fitresults$classx
lambdacoef <- fitresults$lambdacoef
ltaucoef <- fitresults$ltaucoef
if (verbose) cat("current ll from optimisation",currll,"\n")
optll <- currll
oneiteration <- calclikelihood(classx,outcomex,lambdacoef,ltaucoef,
updatemoments=TRUE)
currll <- oneiteration$penlogLik
lastll <- 2*currll
while(abs((lastll-currll)/lastll)>1.0e-7) {
lastll <- currll
momentdata <<- oneiteration$moments
oneiteration <- calclikelihood(classx,outcomex,lambdacoef,ltaucoef,
updatemoments=TRUE)
currll <- oneiteration$penlogLik
if (verbose) cat("current ll",currll,"\n")
}
adaptive <- (abs((currll-optll)/currll)>1.0e-6) || (abs((currll-prevll)/currll)>1.0e-6)
if ((prevll-currll)/abs(currll) > 1.0e-3) stop("divergence - increase quadrature points")
nadaptive <- nadaptive+1
if (nadaptive > 200) stop("too many adaptive iterations - increase quadrature points")
prevll <- currll
zprop <- oneiteration$classprob
}
fitresults <- fitparams(classx,outcomex,lambdacoef,ltaucoef,
calcSE=calcSE,noiterations=500)
return(list(nlm=fitresults$nlm,momentdata=momentdata))
}
for (iclass in 1:nclass) momentdata[[iclass]] <- matrix(c(rep(c(0,1),each=nlevel3),
rep(rep(c(0,1,0),each=nlevel3),times=nlevel2)),
nrow=nlevel3)
if (nclass==1) classx <- NULL
else {
classx <- rep(NA,nclass-1)
initclassp <- ifelse(initclassp<1.0e-4,1.0e-4,initclassp)
initclassp <- ifelse(initclassp>(1.0-1.0e-4),1-1.0e-4,initclassp)
for (i in 2:nclass) classx[i-1] <- log(initclassp[i]/initclassp[1])
}
initoutcomep <- ifelse(initoutcomep<1.0e-4,1.0e-4,initoutcomep)
initoutcomep <- ifelse(initoutcomep>(1.0-1.0e-4),1-1.0e-4,initoutcomep)
if (probit) outcomex <- qnorm(initoutcomep)
else outcomex <- log(initoutcomep/(1-initoutcomep))
if (missing(initlambdacoef) || is.null(initlambdacoef)) {
if (byclass) lambdacoef <- matrix(rep(0,level2size*nclass),nrow=nclass)
else lambdacoef <- rep(0,level2size)
} else lambdacoef <- initlambdacoef
if (missing(initltaucoef) || is.null(initltaucoef)) {
testltaucoef <- -3.0
maxltau <- NA
maxll <- -Inf
repeat {
if (verbose) cat('trying ltaucoef ',testltaucoef,"\n")
if (byclass) theltaucoef <- rep(testltaucoef,nclass)
else theltaucoef <- testltaucoef
onelikelihood <- calclikelihood(classx,outcomex,lambdacoef,theltaucoef)
currll <- onelikelihood$penlogLik
if (verbose) cat("ll",currll,"\n")
if (currll < maxll) break()
maxll <- currll
maxltau <- testltaucoef
testltaucoef <- testltaucoef+0.1
}
if (verbose) cat('using ltaucoef ',maxltau,"\n")
if (byclass) ltaucoef <- rep(maxltau,nclass)
else ltaucoef <- maxltau
}
else ltaucoef <- initltaucoef
myfit <- adaptivefit(classx, outcomex, lambdacoef,ltaucoef)
optim.fit <- myfit$nlm
momentdata <<- myfit$momentdata
if (!calcSE) separ <- rep(NA,length(optim.fit$estimate))
else {
s <- svd(optim.fit$hessian)
separ <- diag(s$v %*% diag(1/s$d) %*% t(s$u))
separ[!is.nan(separ) & (separ>=0.0)] <- sqrt(separ[!is.nan(separ) & (separ>=0.0)])
separ[is.nan(separ) | (separ<0.0)] <- NA
}
if (nclass==1) classp <- 1
else {
classp <-optim.fit$estimate[1:(nclass-1)]
classp <- c(0,classp)
}
outcomex <- matrix(optim.fit$estimate[outcomestart:outcomeend],nrow=nclass)
classp <- exp(classp)/sum(exp(classp))
if (probit) outcomep <- pnorm(outcomex)
else outcomep <- exp(outcomex)/(1+exp(outcomex))
if (byclass) lambdacoef <- matrix(optim.fit$estimate[lambdastart:lambdaend],nrow=nclass)
else lambdacoef <- optim.fit$estimate[lambdastart:lambdaend]
ltaucoef <- optim.fit$estimate[taustart:tauend]
calcrandom <- function() {
outcomex <- log(outcomep/(1-outcomep))
onerandom <- function(x) {
loglik <- function(beta) {
for (i in 1:nclass) {
if (byclass) {
if (probit) outcomep <- pnorm(outcomex[i,]+rep(lambdacoef[i,],nlevel2)*
(beta[1]+exp(ltaucoef[i])*rep(beta[2:(1+nlevel2)],each=nlevel1)))
else outcomep <- 1/(1+exp(-outcomex[i,]-rep(lambdacoef[i,],nlevel2)*
(beta[1]+exp(ltaucoef[i])*rep(beta[2:(1+nlevel2)],each=nlevel1))))
} else {
if (probit) outcomep <- pnorm(outcomex[i,]+rep(lambdacoef,nlevel2)*
(beta[1]+exp(ltaucoef)*rep(beta[2:(1+nlevel2)],each=nlevel1)))
else outcomep <- 1/(1+exp(-outcomex[i,]-rep(lambdacoef,nlevel2)*
(beta[1]+exp(ltaucoef)*rep(beta[2:(1+nlevel2)],each=nlevel1))))
}
oneprob <- t(apply(t(x)*outcomep+t(1-x)*(1-outcomep),2,prod,na.rm=TRUE))
if (i==1) allprob <- oneprob*classp[i]
else allprob <- allprob+oneprob*classp[i]
}
ll <- -(sum(log(allprob))+sum(dnorm(beta,mean=0,sd=1,log=TRUE)))
if (is.nan(ll) || is.infinite(ll)) ll <- 1.0*.Machine$double.xmax
return(ll)
}
beta <- rep(0,1+nlevel2)
optim.fit <- nlm(loglik,beta,print.level=0,iterlim=1000,hessian=TRUE,gradtol=1.0e-7)
if (optim.fit$code >= 3)
warning("Maximum likelihood not found - nlm exited with code ", optim.fit$code, " .\n")
beta <- optim.fit$estimate
sebeta <- sqrt(diag(solve(optim.fit$hessian)))
checkx <- matrix(x,ncol=nlevel1,byrow=T)
checkx <- apply(checkx,1,function(x) any(is.na(x)))
checkx <- c(FALSE,checkx)
beta[checkx] <- NA
sebeta[checkx] <- NA
return(c(beta=beta,sebeta=sebeta))
}
betas <- t(apply(outcomes,1,onerandom))
return(betas)
}
ranef <- calcrandom()
if (byclass) final <- calclikelihood(if (nclass==1) NULL else optim.fit$estimate[1:(nclass-1)],
matrix(optim.fit$estimate[outcomestart:outcomeend],nrow=nclass),
matrix(optim.fit$estimate[lambdastart:lambdaend],nrow=nclass),
optim.fit$estimate[taustart:tauend],
updatemoments=FALSE,calcfitted=TRUE)
else final <- calclikelihood(if (nclass==1) NULL else optim.fit$estimate[1:(nclass-1)],
matrix(optim.fit$estimate[outcomestart:outcomeend],nrow=nclass),
optim.fit$estimate[lambdastart:lambdaend],
optim.fit$estimate[taustart:tauend],
updatemoments=FALSE,calcfitted=TRUE)
fitted <- final$fitted
classprob <- final$classprob
np <- length(optim.fit$estimate)
nobs <- sum(freq)
deviance <- 2*sum(ifelse(freq==0,0,freq*log(freq/fitted)))
if (any(abs(as.vector(outcomex))>20)) warning("Problem in solution, probably unstable")
list(fit=optim.fit,nclass=nclass,classp=classp,outcomep=outcomep,lambdacoef=lambdacoef,taucoef=exp(ltaucoef),se=separ,
np=np,nobs=nobs,logLik=final$logLik,penlogLik=final$penlogLik,freq=freq,fitted=fitted,ranef=ranef
,classprob=classprob,deviance=deviance)
} |
library(knotR)
filename <- "8_19.svg"
a <- reader(filename)
Mver <- matrix(c(
02,18,
03,17,
04,16,
05,15,
07,13,
08,12,
09,11,
14,06
),ncol=2,byrow=TRUE)
ou819 <- matrix(c(
01,06,
02,15,
09,03,
05,10,
06,15,
14,07,
12,18,
16,11
),ncol=2,byrow=TRUE)
sym819 <- symmetry_object(a,Mver=Mver,xver=c(1,10))
jj <-
knotoptim(filename,
symobj = sym819,
ou = ou819,
prob = 0,
iterlim=1000, print.level=2
)
write_svg(jj,filename,safe=FALSE)
dput(jj,file=sub('.svg','.S',filename)) |
fsl_nanm = function(
...,
outfile = tempfile(fileext = ".nii.gz"),
retimg = FALSE
) {
fslnanm(..., outfile = outfile, retimg = retimg)
return(outfile)
} |
test_that("test_sc2pv", {
pfm <- matrix(c(3, 5, 4, 2, 7, 0, 3, 4, 9, 1, 1, 3, 3, 6,
4, 1, 11, 0, 3, 0, 11, 0, 2, 1, 11, 0, 2, 1, 3, 3, 2,
6, 4, 1, 8, 1, 3, 4, 6, 1, 8, 5, 1, 0, 8, 1, 4, 1, 9,
0, 2, 3, 9, 5, 0, 0, 11, 0, 3, 0, 2, 7, 0, 5), nrow = 4,
dimnames = list(c("A", "C", "G", "T")))
bg <- c(A = 0.25, C = 0.25, G = 0.25, T = 0.25)
score <- 8.77
type <- "PFM"
pvalue <- TFMsc2pv(pfm, score, bg, type)
expect_equal(pvalue, 0.00001007156, tolerance=1e-5)
}) |
globalVariables("dot")
Greek <- read.table(system.file("etc/GreekLetters.txt", package="sem"), as.is=TRUE)
math <- function(text, html.only=FALSE, hat=FALSE){
if (length(text) > 1) {
result <- sapply(text, math, html=html.only, hat=hat)
names(result) <- names(text)
return(result)
}
subscripts <- c("&
"&
superscripts <- c("&
"&
names(subscripts) <- names(superscripts) <- 0:9
hat <- if (hat) "&
text <- gsub(" ", "", text)
symbol <- regexpr("^[a-zA-Z]+", text)
if (symbol != 1) stop("text does not start with an alphabetic symbol name")
symbol <- if (html.only) {
paste0("&", substring(text, 1, attr(symbol, "match.length")), ";")
}
else{
s <- substring(text, 1, attr(symbol, "match.length"))
s <- Greek[s, "decimal"]
if (is.na(s)) stop(s, " is not a Greek letter")
s
}
subscript <- regexpr("_\\{", text)
subscript <- if (subscript >= 1){
subscript <- substring(text, subscript + 2)
endbrace <- regexpr("\\}", subscript)
if (endbrace < 1) stop("unmatched closing brace in ", text)
substring(subscript, 1, endbrace - 1)
}
else ""
if (subscript != ""){
subscript <- unlist(strsplit(subscript, split=""))
subscript <- subscripts[subscript]
if (any(is.na(subscript))) stop ("invalid non-numeral subscript")
subscript <- paste(subscript, collapse="")
}
superscript <- regexpr("\\^\\{", text)
superscript <- if (superscript >= 1){
superscript <- substring(text, superscript + 2)
endbrace <- regexpr("\\}", superscript)
if (endbrace < 1) stop("unmatched closing brace in ", text)
substring(superscript, 1, endbrace - 1)
}
else ""
if (superscript != ""){
superscript <- unlist(strsplit(superscript, split=""))
superscript <- superscripts[superscript]
if (any(is.na(superscript))) stop ("invalid non-numeral superscript")
superscript <- paste(superscript, collapse="")
}
paste0(symbol, hat, subscript, superscript)
}
path.diagram <- function(...) {
.Deprecated("pathDiagram", package = "sem")
pathDiagram(...)
}
pathDiagram <- function (model, ...)
{
UseMethod("pathDiagram")
}
pathDiagram.semmod <- function(model, obs.variables, ...) {
parse.path <-
function(path) {
path.1 <- gsub("-", "", gsub(" ","", path))
direction <- if (regexpr("<>", path.1) > 0)
2
else if (regexpr("<", path.1) > 0)
- 1
else if (regexpr(">", path.1) > 0)
1
else
stop(paste("ill-formed path:", path))
path.1 <- strsplit(path.1, "[<>]")[[1]]
list(first = path.1[1], second = path.1[length(path.1)], direction =
direction)
}
if ((!is.matrix(model)) |
ncol(model) != 3)
stop ("model argument must be a 3-column matrix")
startvalues <- as.numeric(model[,3])
par.names <- model[,2]
n.paths <- length(par.names)
heads <- from <- to <- rep(0, n.paths)
for (p in 1:n.paths) {
path <- parse.path(model[p,1])
heads[p] <- abs(path$direction)
to[p] <- path$second
from[p] <- path$first
if (path$direction == -1) {
to[p] <- path$first
from[p] <- path$second
}
}
ram <- matrix(0, p, 5)
all.vars <- unique(c(to, from))
latent.vars <- setdiff(all.vars, obs.variables)
vars <- c(obs.variables, latent.vars)
pars <- na.omit(unique(par.names))
ram[,1] <- heads
ram[,2] <- apply(outer(vars, to, "=="), 2, which)
ram[,3] <- apply(outer(vars, from, "=="), 2, which)
par.nos <- apply(outer(pars, par.names, "=="), 2, which)
if (length(par.nos) > 0)
ram[,4] <-
unlist(lapply(par.nos, function(x)
if (length(x) == 0)
0
else
x))
ram[,5] <- startvalues
colnames(ram) <-
c("heads", "to", "from", "parameter", "start value")
pars <- unique(na.omit(par.names))
coeff <- rep(0, length(pars))
names(coeff) <- pars
fake.sem <-
list(
ram = ram, n = length(obs.variables), var.names = vars, coeff = coeff,
semmod = model
)
class(fake.sem) <- "sem"
pathDiagram(fake.sem, ...)
}
pathDiagram.sem <-
function (model, file = "pathDiagram", style = c("ram", "traditional"),
output.type = c("html", "graphics", "dot"), graphics.fmt = "pdf", dot.options = NULL,
size = c(8, 8), node.font = c("Helvetica", 14),
edge.font = c("Helvetica", 10), digits = 2, rank.direction = c("LR", "TB"),
min.rank = NULL, max.rank = NULL, same.rank = NULL,
variables = model$var.names, var.labels, parameters, par.labels,
ignore.double = TRUE, ignore.self = FALSE, error.nodes = TRUE,
edge.labels = c("names", "values", "both"), edge.colors = c("black", "black"),
edge.weight = c("fixed", "proportional"),
node.colors = c("transparent", "transparent", "transparent"),
standardize = FALSE, ...) {
Dot <- function(..., semicolon = TRUE, newline = TRUE) {
cat(file = handle, paste0(..., if (semicolon)
";"
else
"",
if (newline)
"\n"
else
""))
}
style <- match.arg(style)
output.type <- match.arg(output.type)
edge.labels <- match.arg(edge.labels)
edge.weight <- match.arg(edge.weight)
rank.direction <- match.arg(rank.direction)
if (output.type == "html") {
handle <- textConnection("dot", "w")
}
else {
dot.file <- paste0(file, ".dot")
handle <- file(dot.file, "w")
if (output.type == "graphics")
graph.file <- paste0(file, ".", graphics.fmt)
}
on.exit(close(handle))
Dot("digraph \"", deparse(substitute(model)), "\" {", semicolon = FALSE)
Dot(" rankdir=", rank.direction)
Dot(" size=\"", size[1], ",", size[2], "\"")
Dot(
" node [fontname=\"", node.font[1],
"\" fontsize=", node.font[2], " fillcolor=\"", node.colors[1],
"\" shape=box style=filled]"
)
Dot(" edge [fontname=\"", edge.font[1],
"\" fontsize=", edge.font[2], "]")
Dot(" center=1")
if (!is.null(min.rank)) {
min.rank <- paste0("\"", min.rank, "\"")
min.rank <- gsub(",", "\" \"", gsub(" ", "", min.rank))
Dot(" {rank=min ", min.rank, "}", semicolon = FALSE)
}
if (!is.null(max.rank)) {
max.rank <- paste0("\"", max.rank, "\"")
max.rank <- gsub(",", "\" \"", gsub(" ", "", max.rank))
Dot(" {rank=max ", max.rank, "}", semicolon = FALSE)
}
if (!is.null(same.rank)) {
for (s in 1:length(same.rank)) {
same <- paste0("\"", same.rank[s], "\"")
same <- gsub(",", "\" \"", gsub(" ", "", same))
Dot(" {rank=same ", same, "}", semicolon = FALSE)
}
}
latent <- variables[-(1:model$n)]
for (lat in latent) {
Dot(" \"", lat, "\" [shape=ellipse]", semicolon = FALSE)
}
endogenous <- classifyVariables(model$semmod)$endogenous
endogenous <-
variables[apply(outer(endogenous, model$var.names, "=="), 1, which)]
if (style == "traditional") {
variables <- c(variables, paste0(endogenous, ".error"))
error.color <-
if (length(node.colors) < 3)
node.colors[1]
else
node.colors[3]
}
for (endog in endogenous) {
Dot(" \"", endog, "\" [fillcolor=\"", node.colors[2], "\"]", semicolon =
FALSE)
if (style == "traditional") {
if (error.nodes) Dot(
" \"", endog, ".error\" [shape=ellipse] [fillcolor=\"", error.color, "\"]",
semicolon = FALSE
)
else Dot(
" \"", endog,
".error\" [shape=ellipse width=0 height=0 fixedsize=true label=\"\"] [fillcolor=\"",
error.color, "\"]",
semicolon = FALSE
)
}
}
ram <- model$ram
if (missing(parameters)) {
par.names <- names(coef(model))
rownames(ram)[ram[, "parameter"] != 0] <-
par.names[ram[, "parameter"]]
rownames(ram)[ram[, "parameter"] == 0] <-
ram[ram[, "parameter"] == 0, "start value"]
parameters <- rownames(ram)
}
if (standardize)
ram[, 5] <- stdCoef(model)[, 2]
else
ram[names(model$coeff), 5] <- model$coeff
coefs <- ram[, 5]
na.coefs <- is.na(coefs)
if (any(na.coefs)) {
for (coef in which(na.coefs)) {
ram[coef, 5] <-
(ram[ram[coef, 4] == ram[, 4], 5])[1]
}
}
values <- round(ram[, 5], digits)
heads <- ram[, 1]
to <- ram[, 2]
from <- ram[, 3]
if (!missing(par.labels)) {
check <- names(par.labels) %in% parameters
if (any(!check)) {
msg <- if (sum(!check) > 1)
paste(
"The following parameters do not appear in the model:", paste(names(par.labels)[!check], collapse =
", ")
)
else
paste("The following parameter does not appear in the model:", names(par.labels)[!check])
warning(msg)
par.labels <- par.labels[check]
}
names(parameters) <- parameters
parameters[names(par.labels)] <- par.labels
}
labels <- if (edge.labels == "names")
parameters
else if (edge.labels == "values")
values
else
paste(parameters, values, sep = "=")
colors <- ifelse(values > 0, edge.colors[1], edge.colors[2])
direction <- ifelse((heads == 2), " dir=both", "")
lineweight <- rep(1, nrow(ram))
if (edge.weight == "proportional") {
lineweight <- abs(values) / mean(values)
if (!standardize)
warning("proportional edge weights requested for an unstandardized model")
}
if (style == "ram") {
for (par in 1:nrow(ram)) {
if ((!ignore.double) || (heads[par] == 1)) {
if (ignore.self && to[par] == from[par]) next
Dot(
" \"", variables[from[par]],
"\" -> \"", variables[to[par]], "\" [label=\"",
labels[par], "\"", direction[par], " color=", colors[par],
" penwidth=", round(lineweight[par] + 0.001, 3), "]"
)
}
}
}
else
for (par in 1:nrow(ram)) {
if (heads[par] == 1) {
Dot(
" \"", variables[from[par]],
"\" -> \"", variables[to[par]], "\" [label=\"",
labels[par], "\"", direction[par], " color=", colors[par],
" penwidth=", round(lineweight[par] + 0.001, 3), "]"
)
}
else if (variables[to[par]] %in% endogenous) {
if (variables[to[par]] == variables[from[par]]) {
lab <- labels[par]
val <-
round(sqrt(values[par]), digits = digits)
lab <-
if (edge.labels == "names")
paste0("sqrt(", lab, ")")
else if (edge.labels == "values")
val
else
paste0("sqrt(", parameters[par], ")=", val)
Dot(
" \"", variables[to[par]], ".error\" -> \"",
variables[to[par]], "\" [color=", edge.colors[1], " label=\"", lab,
"\" penwidth=", round(sqrt(lineweight[par]) + 0.001, 3)," ]"
)
}
else{
Dot(
" \"", variables[to[par]], ".error\" -> \"",
variables[from[par]], ".error\" [dir=both label=\"", labels[par],
"\" color=", colors[par],
" penwidth=", round(lineweight[par] + 0.001, 3), "]"
)
}
}
else if (!ignore.double &&
(variables[to[par]] != variables[from[par]])) {
Dot(
" \"", variables[from[par]],
"\" -> \"", variables[to[par]], "\" [label=\"",
labels[par], "\"", direction[par], " color=", colors[par],
" penwidth=", round(lineweight[par] + 0.001, 3), "]"
)
}
}
if (!missing(var.labels)) {
check <- names(var.labels) %in% variables
if (any(!check)) {
msg <- if (sum(!check) > 1)
paste(
"The following variables do not appear in the model:", paste(names(var.labels)[!check], collapse =
", ")
)
else
paste("The following variable does not appear in the model:", names(var.labels)[!check])
warning(msg)
var.labels <- var.labels[check]
}
Dot(" // variable labels: ", semicolon = FALSE)
lines <-
paste0(' "', names(var.labels), '" [label="', var.labels, '"];\n')
Dot(paste(lines, collapse = ""), semicolon = FALSE, newline = FALSE)
}
Dot("}", semicolon = FALSE)
if (output.type == "graphics") {
cmd <-
paste0("dot -T", graphics.fmt, " -o ", graph.file, " -Gcharset=latin1 ",
dot.options, " ", dot.file)
cat("Running ", cmd, "\n")
result <- try(system(cmd))
}
if (output.type == "html" && requireNamespace("DiagrammeR")) {
print(DiagrammeR::DiagrammeR(textConnection(dot), type = "grViz"))
}
result <-
if (output.type == "html")
dot
else
readLines(dot.file)
invisible(result)
} |
dateNumToMillis <- function(datenum) {
datenum * 1000
} |
test_that("wkb_meta() works", {
expect_identical(
wkb_meta(wkt_translate_wkb("POINT (30 10)")),
tibble::tibble(
feature_id = 1L,
part_id = 1L,
type_id = 1L,
size = 1L,
srid = NA_integer_,
has_z = FALSE,
has_m = FALSE
)
)
})
test_that("wkt_meta() works", {
expect_identical(
wkt_meta("POINT (30 10)"),
tibble::tibble(
feature_id = 1L,
part_id = 1L,
type_id = 1L,
size = 1L,
srid = NA_integer_,
has_z = FALSE,
has_m = FALSE
)
)
})
test_that("wkt_streamer_meta() works", {
expect_identical(
wkt_streamer_meta("POINT (30 10)"),
tibble::tibble(
feature_id = 1L,
part_id = 1L,
type_id = 1L,
size = NA_integer_,
srid = NA_integer_,
has_z = FALSE,
has_m = FALSE
)
)
expect_identical(
wkt_streamer_meta("MULTIPOINT ((30 10))", recursive = FALSE),
tibble::tibble(
feature_id = 1L,
part_id = 1L,
type_id = 4L,
size = NA_integer_,
srid = NA_integer_,
has_z = FALSE,
has_m = FALSE
)
)
expect_identical(
wkt_streamer_meta("MULTIPOINT ((30 10))", recursive = TRUE),
tibble::tibble(
feature_id = c(1L, 1L),
part_id = c(1L, 2L),
type_id = c(4L, 1L),
size = c(NA_integer_, NA_integer_),
srid = c(NA_integer_, NA_integer_),
has_z = c(FALSE, FALSE),
has_m = c(FALSE, FALSE)
)
)
expect_identical(
wkt_streamer_meta("GEOMETRYCOLLECTION (POINT (30 10))", recursive = FALSE),
tibble::tibble(
feature_id = 1L,
part_id = 1L,
type_id = 7L,
size = NA_integer_,
srid = NA_integer_,
has_z = FALSE,
has_m = FALSE
)
)
expect_identical(
wkt_streamer_meta(c("GEOMETRYCOLLECTION (POINT (30 10))", NA), recursive = TRUE),
tibble::tibble(
feature_id = c(1L, 1L, 2L),
part_id = c(1L, 2L, NA_integer_),
type_id = c(7L, 1L, NA_integer_),
size = c(NA_integer_, NA_integer_, NA_integer_),
srid = c(NA_integer_, NA_integer_, NA_integer_),
has_z = c(FALSE, FALSE, NA),
has_m = c(FALSE, FALSE, NA)
)
)
})
test_that("wkt_streamer_meta() works with NULLs", {
expect_identical(
wkt_streamer_meta(NA),
tibble::tibble(
feature_id = 1L,
part_id = NA_integer_,
type_id = NA_integer_,
size = NA_integer_,
srid = NA_integer_,
has_z = NA,
has_m = NA
)
)
})
test_that("wkt_meta() counts coordinates when NULLs are present", {
expect_identical(
wkt_meta(c("LINESTRING (20 20, 0 0)", NA)),
tibble::tibble(
feature_id = c(1L, 2L),
part_id = c(1L, NA_integer_),
type_id = c(2L, NA_integer_),
size = c(2L, NA_integer_),
srid = c(NA_integer_, NA_integer_),
has_z = c(FALSE, NA),
has_m = c(FALSE, NA)
)
)
})
test_that("wkt_streamer_meta() returns SRIDs if present", {
expect_identical(
wkt_streamer_meta("SRID=33;POINT (30 10)"),
tibble::tibble(
feature_id = 1L,
part_id = 1L,
type_id = 1L,
size = NA_integer_,
srid = 33L,
has_z = FALSE,
has_m = FALSE
)
)
})
test_that("wkt_streamer_meta() fails on parse error", {
expect_error(wkt_streamer_meta("NOPE"), class = "WKParseException")
})
test_that("geometry type converters work", {
types_str <- c(
"point", "linestring", "polygon",
"multipoint", "multilinestring", "multipolygon",
"geometrycollection"
)
expect_identical(wk_geometry_type_id(types_str), 1:7)
expect_identical(wk_geometry_type(7:1), rev(types_str))
}) |
if (interactive()) pkgload::load_all(".")
run_test <- fritools::is_running_on_fvafrcu_machines()
test_repo <- function() {
d <- tempfile()
dir.create(d)
RUnit::checkTrue(!packager:::is_git_repository(d))
RUnit::checkTrue(!packager:::is_git_clone(d))
RUnit::checkTrue(!packager:::uses_git(d))
gert::git_init(d)
RUnit::checkTrue(packager:::is_git_repository(d))
RUnit::checkTrue(packager:::is_git_clone(d))
RUnit::checkTrue(packager:::uses_git(d))
RUnit::checkTrue(!packager:::is_git_uncommitted(d))
fritools::touch(file.path(d, "foobar.txt"))
result <- packager:::git_add(path = d, files = "foobar.txt")
RUnit::checkTrue(is(result, "tbl"))
result <- packager:::git_commit(d, message = "ini")
RUnit::checkTrue(is(result, "character"))
fritools::touch(file.path(d, "foobaz.txt"))
result <- git_add_commit(d, message = "sec", untracked = TRUE)
RUnit::checkTrue(is(result, "character"))
}
if (interactive()) test_repo()
test_git_tag_create <- function() {
if (run_test) {
path <- file.path(tempdir(), "prutp")
on.exit(unlink(path, recursive = TRUE))
packager:::package_skeleton(path)
RUnit::checkException(packager:::git_tag_create(path = path,
version = "0.0.0",
message = "Foo"))
packager:::use_git(path)
result <- packager:::git_tag_create(path = path,
version = "0.0.0.9000",
message = "Initial Commit")
RUnit::checkIdentical("0.0.0.9000", getElement(result, "name"))
desc::desc_bump_version("minor", file = path)
RUnit::checkException(packager::git_tag(path = path))
}
}
if (interactive()) test_git_tag_create()
test_git_tag <- function() {
if (run_test) {
path <- file.path(tempdir(), "prutp")
on.exit(unlink(path, recursive = TRUE))
packager:::package_skeleton(path)
RUnit::checkException(packager::git_tag(path = path))
packager:::use_git(path)
result <- packager::git_tag(path = path)
RUnit::checkIdentical("1.0", getElement(result, "name"))
desc::desc_bump_version("minor", file = path)
RUnit::checkException(packager::git_tag(path = path))
git_add_commit(path = path)
result <- packager::git_tag(path = path)
RUnit::checkIdentical("1.1", getElement(result, "name"))
desc::desc_set(Version = "0.3", file = path)
git_add_commit(path = path)
RUnit::checkException(packager::git_tag(path = path))
}
}
if (interactive()) test_git_tag() |
library(hamcrest)
x <- 1
y <- asS4(x, TRUE)
z <- asS4(y, FALSE)
assertThat(isS4(x), identicalTo(FALSE))
assertThat(isS4(y), identicalTo(TRUE))
assertThat(isS4(z), identicalTo(FALSE)) |
kern.fun.default <-
function(x,t,h,type_data=c("discrete","continuous"),
ker=c("bino","triang","dirDU","BE","GA","LN","RIG"),a0=0,a1=1,a=1,c=2,...)
{
if (missing(type_data)) stop("argument 'type_data' is omitted")
if ((type_data=="discrete") & (ker=="GA"||ker=="LN"||ker=="BE" ||ker=="RIG"))
stop(" Not appropriate kernel for type_data")
if ((type_data=="continuous") & (ker=="bino"||ker=="triang"||ker=="dirDU"))
stop(" Not appropriate kernel for 'type_data'")
if ((type_data=="discrete") & missing(ker)) ker<-"bino"
if ((type_data=="continuous") & missing(ker)) ker<-"GA"
kx <- kef(x,t,h,type_data,ker,a0,a1,a,c)
structure(list(kernel = ker,x=x,t=t,kx=kx),class="kern.fun")
} |
manipulatorExecute <- function(manipulator)
{
result <- withVisible(eval(manipulator$.code, envir = manipulator))
if (result$visible)
{
eval(print(result$value), enclos=parent.env(manipulator))
}
}
manipulatorSave <- function(manipulator, filename)
{
suppressWarnings(save(manipulator, file=filename))
}
manipulatorLoad <- function(filename)
{
load(filename)
get("manipulator")
}
hasActiveManipulator <- function()
{
.Call(getNativeSymbolInfo("rs_hasActiveManipulator", PACKAGE=""))
}
activeManipulator <- function()
{
.Call(getNativeSymbolInfo("rs_activeManipulator", PACKAGE=""))
}
ensureManipulatorSaved <- function()
{
.Call(getNativeSymbolInfo("rs_ensureManipulatorSaved", PACKAGE=""))
}
createUUID <- function()
{
.Call(getNativeSymbolInfo("rs_createUUID", PACKAGE=""))
}
executeAndAttachManipulator <- function(manipulator)
{
.Call(getNativeSymbolInfo("rs_executeAndAttachManipulator", PACKAGE=""),
manipulator)
}
setManipulatorValue <- function(manipulator, name, value)
{
assign(name, value, envir = get(".userVisibleValues", envir = manipulator))
underlyingValue <- value
controls <- get(".controls", envir = manipulator)
control <- controls[[name]]
if (inherits(control, "manipulator.picker"))
underlyingValue <- (control$values[[value]])
assign(name, underlyingValue, envir = manipulator)
}
userVisibleValues <- function(manipulator, variables)
{
mget(variables, envir = get(".userVisibleValues", envir = manipulator))
}
buttonNames <- function(manipulator)
{
if (exists(".buttonNames", envir = manipulator))
get(".buttonNames", envir = manipulator)
else
character()
}
trackingMouseClicks <- function(manipulator)
{
exists(".mouseClick", envir = manipulator)
}
setMouseClick <- function(manipulator,
deviceX,
deviceY,
userX,
userY,
ndcX,
ndcY)
{
mouseClick <- list(deviceX = deviceX,
deviceY = deviceY,
userX = userX,
userY = userY,
ndcX = ndcX,
ndcY = ndcY)
assign(".mouseClick", mouseClick, envir = manipulator)
}
clearMouseClick <- function(manipulator)
{
assign(".mouseClick", NULL, envir = manipulator)
}
resolveVariableArguments <- function(args)
{
if ( (length(args) == 1L) &&
is.list(args[[1L]]) &&
(is.null(names(args)) || (names(args)[[1L]] == "")) )
{
return (args[[1L]])
}
else
{
return (args)
}
} |
get_restriction_matrix <- function(restriction_matrix, k){
if(!is.null(restriction_matrix)){
restriction_matrix = as.matrix(restriction_matrix)
if(dim(restriction_matrix)[1] == k & dim(restriction_matrix)[2] == k){
restriction_matrix = restriction_matrix
}else if(dim(restriction_matrix)[1] == k^2 | dim(restriction_matrix)[2] == k^2){
ones = matrix(rep(1,k*k),k,k)
restriction_matrix = c(ones) %*% restriction_matrix
restriction_matrix = matrix(restriction_matrix, ncol = k)
restriction_matrix[restriction_matrix == 1] <- NA
}else{
stop(paste0("Different restriction matrix dimension than B. Please use either of the two valid formats containing either kxk (", k,"x",k, ") or k^2xk^2 (", k^2,"x",k^2, ") dimensions."))
}
}else{
restriction_matrix <- matrix(NA, k, k)
}
return(restriction_matrix)
} |
barplot.conData <- function(height,
plottype='RelFreq',
color=NULL,
legend=TRUE,...){
height.data <- height$probs
varNames <- height$varNames
if (plottype == 'All'){
if(length(color)<3){
color = c('darkslategray1','darkslategray3','darkslategray4')
}else if (length(color) > 3){
color=color[1:3]
}
barplot(t(height.data)[,nrow(height.data):1],
main = "" ,
xlab = "" ,
ylab = "" ,
names.arg = rev(varNames) ,
col = color ,
las = 2 , cex.axis = 0.8 ,
beside=TRUE ,
horiz=TRUE)
if(legend==TRUE) legend('topright', c("rel.freq","p1|1","p1|0"), fill = color,cex=0.7)
}else if (plottype == 'RelFreq'){
if(is.null(color)) color='darkslategray3'
barplot(rev(height.data[,1]) ,
main = "" ,
xlab = "",
ylab = "",
names.arg = rev(varNames),
col = rev(color),
las = 2 , cex.axis = 1 ,
beside = TRUE , horiz = TRUE)
}
} |
`getTargetSGPContentArea` <-
function(grade,
content_area,
state,
max.sgp.target.years.forward,
my.sgp.target.content_area) {
if (is.null(SGP::SGPstateData[[state]][["SGP_Configuration"]][["content_area.projection.sequence"]][[content_area]])) {
return(content_area)
} else {
tmp.content_areas.by.grade <- paste(SGP::SGPstateData[[state]][["SGP_Configuration"]][["content_area.projection.sequence"]][[content_area]],
SGP::SGPstateData[[state]][["SGP_Configuration"]][["grade.projection.sequence"]][[content_area]], sep=".")
tmp.index <- match(paste(content_area, grade, sep="."), tmp.content_areas.by.grade)
if (is.na(tmp.index)) {
message(paste0("\tNOTE: '", content_area, "', GRADE '", grade, "' combination is not in current @Data. Will return ", content_area, " for '", my.sgp.target.content_area, "'."))
return(content_area)
} else {
return(SGP::SGPstateData[[state]][["SGP_Configuration"]][["content_area.projection.sequence"]][[content_area]][
min(tmp.index+max.sgp.target.years.forward, length(SGP::SGPstateData[[state]][["SGP_Configuration"]][["grade.projection.sequence"]][[content_area]]))])
}
}
} |
"maboost"<-
function(x,...)UseMethod("maboost") |
modify_call <- function(call, new_args) {
stopifnot(is.call(call), is.list(new_args))
call <- standardise_call(call)
nms <- names(new_args) %||% rep("", length(new_args))
if (any(nms == "")) {
stop("All new arguments must be named", call. = FALSE)
}
for(nm in nms) {
call[[nm]] <- new_args[[nm]]
}
call
} |
ttsLSTM <- function (y,x=NULL,train.end,arOrder=1,xregOrder=0,type, memoryLoops=10,shape=NULL,dim3=5){
if (!is.zoo(y)) {print("The data must be a zoo object.")}
if (max(diff(unique(y)))==min(diff(unique(y))))
{stop("Binary dependent variable is not allowed for current version")}
y=timeSeries::as.timeSeries(y)
if (!is.null(x)) {
x=timeSeries::as.timeSeries(x)
if ( nrow(y) != nrow(x) ) {print("Variables must have the same rows.")
}
}
if (is.null(train.end)) {print("The train.end must be specified.") }
train.start=start(y)
t0=which(as.character(time(y))==train.end)
test.start=as.character(time(y))[t0+1]
test.end=as.character(end(y))
p=max(arOrder,xregOrder)
colNAMES=c(outer(paste0(names(x),"_L"),0:p,FUN=paste0))
if (p==0) {
y=y
datasetX=timeSeries::as.timeSeries(x)
ar0=NULL
} else {
datasetY=timeSeries::as.timeSeries(embed(y,p+1),time(y)[-c(1:p)])
y=datasetY[,1]
ar0=datasetY[,-1]
colnames(ar0)=paste0("ar",1:p)
if (is.null(x)) {datasetX=NULL
} else {
datasetX=timeSeries::as.timeSeries(embed(x,p+1),time(x)[-c(1:p)])
colnames(datasetX)=colNAMES
}
}
colnames(y)="y"
if (min(arOrder)==0) {ar=NULL
} else {ar=ar0[,paste0("ar",arOrder)]}
if (is.null(x)) {X=datasetX} else {
L.ID=paste0("L",xregOrder)
IDx=NULL
for (i in L.ID) {IDx=c(IDx,grep(colNAMES,pattern=i))}
X=datasetX[,IDx]
}
DF <- na.omit(cbind(y,ar,X))
trend <- 1:nrow(y)
if (timeSeries::isRegular(y)) {
seasonDummy <- data.frame(forecast::seasonaldummy(as.ts(y)))
DF0 <- cbind(ar0,X,seasonDummy,trend)
} else {DF0 <- cbind(ar0,X,trend)}
if (type=="trend") {DF<-cbind(DF,trend)} else if (type=="sesaon") {DF<-cbind(DF,seasonDummy)
} else if (type=="both") {DF<-cbind(DF,trend,seasonDummy)
} else {DF <- DF}
newData= timeSeries::as.timeSeries(DF)
trainData=window(newData,start=train.start,end=train.end)
testData=window(newData,start=test.start,end=test.end)
train0 = data.frame(value = as.numeric(trainData[,1]), trainData[,-1])
train = train0[complete.cases(train0), ]
test0 = data.frame(value = as.numeric(testData[,1]), testData[,-1])
test = test0[complete.cases(test0), ]
batch.size = DescTools::GCD(nrow(train),nrow(test))
nrow(train)/batch.size; nrow(test)/batch.size
names(train)
train.new=as.matrix(train)
dimnames(train.new)=NULL
test.new=as.matrix(test)
dimnames(test.new)=NULL
if(is.null(shape)) {SHAPE=ncol(train.new)} else {SHAPE=shape}
k=dim3
x.train = array(data = train.new[,-1], dim = c(nrow(train.new), SHAPE, k))
y.train = array(data = train.new[,1], dim = c(nrow(train.new), 1))
x.test = array(data = test.new[,-1], dim = c(nrow(test.new), SHAPE, k))
y.test = array(data = test.new[,1], dim = c(nrow(test.new), 1))
model <- keras::keras_model_sequential()
model %>%
keras::layer_lstm(units = 100,
input_shape = c(SHAPE, k),
batch_size = batch.size,
return_sequences = TRUE,
stateful = TRUE) %>%
keras::layer_dropout(rate = 0.5) %>%
keras::layer_lstm(units = 50,
return_sequences = FALSE,
stateful = TRUE) %>%
keras::layer_dropout(rate = 0.5) %>%
keras::layer_dense(units = 1)
model %>%
keras::compile(loss = 'mae', optimizer = 'adam')
for(i in 1:memoryLoops){
model %>% keras::fit(x = x.train,
y = y.train,
batch_size = batch.size,
epochs = 1,
verbose = 0,
shuffle = FALSE)
model %>% keras::reset_states()}
return(list(output=model,batch.size=batch.size,k=k,SHAPE=SHAPE,arOrder=arOrder,data=cbind(y,DF0),dataused=DF))
} |
extrapolation <-
function (results, lambda, lambda0, estimate0, fitting.method, B, parameter) {
if (parameter == 'inbreeding'){
inb <- t(results$inb_dep)
se_inb <- t(results$se_inb_dep)
inb_ave<-colMeans(inb, na.rm = TRUE)
se_inb_ave<-colMeans(se_inb, na.rm = TRUE)
lambda<-c(lambda0, lambda)
inb_ave<-c(estimate0$inb0, inb_ave)
se_inb<-c(estimate0$se0, se_inb_ave)
p.names<-c("inb", "se_inb")
estimates<-data.frame(inb_ave, se_inb)
colnames(estimates) <- p.names
inb_pred <- c()
inb_pred_se <- c()
AIC <- c()
se_pred <- c()
var <- c()
for (i in 1: length(fitting.method)) {
f <- fitting.method[i]
if (f == 'line') {
extrapolation_inb <- lm(estimates[ ,1] ~ lambda)
inb_pred[i] <- predict(extrapolation_inb, newdata = data.frame(lambda = 0))
extrapolation_inb_se <- lm(estimates[,2] ~ lambda)
inb_pred_se[i] <- predict(extrapolation_inb_se, newdata = data.frame(lambda = 0))
AIC[i] <- AIC(extrapolation_inb)
}
if (f == 'quad') {
extrapolation_inb1<- lm(estimates[ ,1] ~ lambda+ I(lambda^2))
inb_pred[i] <- predict(extrapolation_inb1, newdata = data.frame(lambda = 0))
extrapolation_inb1_se<- lm(estimates[ ,2] ~ lambda+ I(lambda^2))
inb_pred_se[i] <-predict(extrapolation_inb1_se, newdata = data.frame(lambda = 0))
AIC[i] <- AIC(extrapolation_inb1)
}
if (f == 'nonl') {
extrapolation_inb2<-fit.nls(lambda, p.names, estimates[, ])
inb_pred[i] <-predict(extrapolation_inb2[[1]], newdata = data.frame(lambda = 0))
inb_pred_se[i] <-predict(extrapolation_inb2[[2]], newdata = data.frame(lambda = 0))
AIC[i] <- AIC(extrapolation_inb2)
}
if (f == 'cubi') {
extrapolation_inb<- lm(estimates[ ,1] ~ lambda+ I(lambda^2)+ I(lambda^3))
inb_pred[i] <-predict(extrapolation_inb, newdata = data.frame(lambda = 0))
extrapolation_inb_se<- lm(estimates[,2] ~ lambda+ I(lambda^2)+ I(lambda^3))
inb_pred_se[i] <-predict(extrapolation_inb_se, newdata = data.frame(lambda = 0))
AIC[i] <- AIC(extrapolation_inb)
}
S <- c()
for ( ii in 1:length(inb[ 1,])) {
diff <- c()
for ( j in 1: B ) {
diff[j] <- inb[j,ii]-inb_ave[ii]
}
diff <- na.omit(diff)
S[ii] <- 1/(B-1)*sum(diff^2)
}
lambda1 <- lambda[-1]
if (f == 'line') {
extrapolation_var <- lm(S ~ lambda1)
se_pred[i] <- predict(extrapolation_var, newdata = data.frame(lambda1 = 0))
}
if (f == 'quad') {
extrapolation_var1 <- lm(S ~ lambda1+ I(lambda1^2))
se_pred[i] <- predict(extrapolation_var1, newdata = data.frame(lambda1 = 0))
}
if (f == 'nonl') {
p.names1 <- c("S")
estimates1 <- data.frame(S)
colnames(estimates1) <- p.names1
ex <- fit.nls(lambda1, p.names1, estimates1[ ])
se_pred[i] <- predict(ex[[1]], newdata = data.frame(lambda1 = 0))
}
if (f == 'cubi') {
extrapolation_var1 <- lm(S ~ lambda1+ I(lambda1^2)+ I(lambda1^3))
se_pred[i] <- predict(extrapolation_var1, newdata = data.frame(lambda1 = 0))
}
var[i] <- inb_pred_se[i]^2 + se_pred[i]^2
}
extrapolation <- list()
extrapolation$fitting.method <- fitting.method
extrapolation$inb_dep <- inb_pred
extrapolation$se <- inb_pred_se
extrapolation$sampling_se <- se_pred
extrapolation$var <- var
extrapolation$AIC <- AIC
}
if (parameter =='heritability'){
h <- t(results$heritability)
se_h <- t(results$se_h)
VA <- t(results$VA)
VE <- t(results$VE)
h_ave<-colMeans(h, na.rm = TRUE)
se_h_ave<-colMeans(se_h, na.rm = TRUE)
VA_ave<-colMeans(VA, na.rm = TRUE)
VE_ave<-colMeans(VE, na.rm = TRUE)
lambda <- c(lambda0, lambda)
h_ave <- c(estimate0$h0, h_ave)
se_h <- c(estimate0$seh0, se_h_ave)
VA <- c(estimate0$VA0, VA_ave)
VE <- c(estimate0$VE0, VE_ave)
p.names<-c("h", "se_h", "VA", "VE")
estimates<-data.frame(h_ave, se_h, VA, VE)
colnames(estimates) <- p.names
h_pred <- c()
h_pred_se <- c()
VA_pred <- c()
VE_pred <- c()
AIC <- c()
aicc <- c()
se_pred <- c()
var <- c()
for (i in 1: length(fitting.method)) {
f <- fitting.method[i]
if (f == 'line') {
extrapolation_h<- lm(estimates[ ,1] ~ lambda)
h_pred[i] <-predict(extrapolation_h, newdata = data.frame(lambda = 0))
extrapolation_h_se <- lm(estimates[,2] ~ lambda)
h_pred_se[i] <- predict(extrapolation_h_se, newdata = data.frame(lambda = 0))
extrapolation_VA <- lm(estimates[ ,3] ~ lambda)
VA_pred[i] <-predict(extrapolation_VA, newdata = data.frame(lambda = 0))
extrapolation_VE<- lm(estimates[ ,4] ~ lambda)
VE_pred[i] <-predict(extrapolation_VE, newdata = data.frame(lambda = 0))
AIC[i] <- AIC(extrapolation_h)
}
if (f == 'quad') {
extrapolation_h<- lm(estimates[ ,1] ~ lambda+ I(lambda^2))
h_pred[i] <-predict(extrapolation_h, newdata = data.frame(lambda = 0))
extrapolation_h_se<- lm(estimates[ ,2] ~ lambda+ I(lambda^2))
h_pred_se[i] <-predict(extrapolation_h_se, newdata = data.frame(lambda = 0))
extrapolation_VA <- lm(estimates[ ,3] ~ lambda+ I(lambda^2))
VA_pred[i] <-predict(extrapolation_VA, newdata = data.frame(lambda = 0))
extrapolation_VE<- lm(estimates[ ,4] ~ lambda+ I(lambda^2))
VE_pred[i] <-predict(extrapolation_VE, newdata = data.frame(lambda = 0))
AIC[i] <- AIC(extrapolation_h)
}
if (f == 'nonl') {
extrapolation_h<-fit.nls(lambda, p.names, estimates[, ])
h_pred[i] <-predict(extrapolation_h[[1]], newdata = data.frame(lambda = 0))
h_pred_se[i] <-predict(extrapolation_h[[2]], newdata = data.frame(lambda = 0))
VA_pred[i] <-predict(extrapolation_h[[3]], newdata = data.frame(lambda = 0))
VE_pred[i] <-predict(extrapolation_h[[4]], newdata = data.frame(lambda = 0))
}
if (f == 'cubi') {
extrapolation_h<- lm(estimates[ ,1] ~ lambda+ I(lambda^2)+I(lambda^3))
h_pred[i] <-predict(extrapolation_h, newdata = data.frame(lambda = 0))
extrapolation_h_se<- lm(estimates[ ,2] ~ lambda+ I(lambda^2)+I(lambda^3))
h_pred_se[i] <-predict(extrapolation_h_se, newdata = data.frame(lambda = 0))
extrapolation_VA <- lm(estimates[ ,3] ~ lambda+ I(lambda^2)+I(lambda^3))
VA_pred[i] <-predict(extrapolation_VA, newdata = data.frame(lambda = 0))
extrapolation_VE<- lm(estimates[ ,4] ~ lambda+ I(lambda^2)+I(lambda^3))
VE_pred[i] <-predict(extrapolation_VE, newdata = data.frame(lambda = 0))
AIC[i] <- AIC(extrapolation_h)
}
S <- c()
for ( ii in 1:length(h[ 1, ])) {
diff <- c()
for ( j in 1: B ) {
diff[j] <- h[j,ii]-h_ave[ii]
}
diff <- na.omit(diff)
S[ii] <- 1/(B-1)*sum(diff^2)
}
lambda1 <- lambda[-1]
if (f == 'line') {
extrapolation_var <- lm(S ~ lambda1)
se_pred[i] <- predict(extrapolation_var, newdata = data.frame(lambda1 = 0))
}
if (f == 'quad') {
extrapolation_var1 <- lm(S ~ lambda1+ I(lambda1^2))
se_pred[i] <- predict(extrapolation_var1, newdata = data.frame(lambda1 = 0))
}
if (f == 'nonl') {
p.names1 <- c("S")
estimates1 <- data.frame(S)
colnames(estimates1) <- p.names1
ex <- fit.nls(lambda1, p.names1, estimates1[ ])
se_pred[i] <- predict(ex[[1]], newdata = data.frame(lambda1 = 0))
}
if (f == 'cubi') {
extrapolation_var1 <- lm(S ~ lambda1+ I(lambda1^2)+I(lambda1^3))
se_pred[i] <- predict(extrapolation_var1, newdata = data.frame(lambda1 = 0))
}
var[i] <- h_pred_se[i]^2 + se_pred[i]^2
}
extrapolation <- list()
extrapolation$fitting.method <- fitting.method
extrapolation$h <- h_pred
extrapolation$se <- h_pred_se
extrapolation$sampling_se <- se_pred
extrapolation$var <- var
extrapolation$VA <- VA_pred
extrapolation$VE <- VE_pred
extrapolation$AIC <- AIC
}
return(extrapolation)
} |
sf_upsert_metadata <- function(metadata_type,
metadata,
control = list(...), ...,
all_or_none = deprecated(),
verbose = FALSE){
which_operation <- "upsertMetadata"
metadata <- metadata_type_validator(obj_type = metadata_type, obj_data = metadata)
control_args <- return_matching_controls(control)
control_args$api_type <- "Metadata"
control_args$operation <- "upsert"
if(is_present(all_or_none)) {
deprecate_warn("0.1.3", "salesforcer::sf_upsert_metadata(all_or_none = )",
"sf_upsert_metadata(AllOrNoneHeader = )",
details = paste0("You can pass the all or none header directly ",
"as shown above or via the `control` argument."))
control_args$AllOrNoneHeader <- list(allOrNone = tolower(all_or_none))
}
control <- do.call("sf_control", control_args)
operation_node <- newXMLNode(which_operation,
namespaceDefinitions = c('http://soap.sforce.com/2006/04/metadata'),
suppressNamespaceWarning = TRUE)
xml_dat <- build_metadata_xml_from_list(input_data = metadata, metatype = metadata_type, root = operation_node)
base_metadata_url <- make_base_metadata_url()
root <- make_soap_xml_skeleton(soap_headers = control, metadata_ns = TRUE)
body_node <- newXMLNode("soapenv:Body", parent = root)
body_node <- addChildren(body_node, xml_dat)
request_body <- as(root, "character")
httr_response <- rPOST(url = base_metadata_url,
headers = c("SOAPAction"=which_operation,
"Content-Type"="text/xml"),
body = request_body)
if(verbose){
make_verbose_httr_message(httr_response$request$method,
httr_response$request$url,
httr_response$request$headers,
request_body)
}
catch_errors(httr_response)
response_parsed <- content(httr_response, encoding="UTF-8")
resultset <- response_parsed %>%
xml_ns_strip() %>%
xml_find_all('.//result') %>%
map_df(xml_nodeset_to_df) %>%
type_convert(col_types = cols())
return(resultset)
} |
library(neuralnet)
concrete<-read.csv(file = "concrete.txt",stringsAsFactors = F)
normalize<-function(x){
return((x-min(x))/(max(x)-min(x)))
}
concrete<-as.data.frame(lapply(concrete, normalize))
concrete_train<-concrete[1:773,]
concrete_test<-concrete[774:1030,]
concrete_model<-neuralnet(strength~cement+slag+ash+water+superplastic+coarseagg+fineagg+age,data = concrete_train,hidden = 5)
model_res<-compute(concrete_model,concrete_test[,1:8])
x=model_res$net.result
y=concrete_test$strength
cor(x,y)
plot(concrete_model) |
dput_levels <- function(vec){
if (is.factor(vec)) return(dput(levels(vec)))
if (!is.factor(vec)) return(dput(unique(vec)))
} |
filterarchids_warning <-
function(archs_ids, filter) {
my_labelsIds <- vector(mode = "character")
for(id in archs_ids) {
sid = entrez_summary(db = "sparcle", id = id)
if(length(sid) > 2) {
if(sum(str_count(sid$displabel, filter)) == length(filter) ) {
my_labelsIds <- c(my_labelsIds, id)
}
}
}
my_labelsIds
} |
context("Checking children")
test_that("children ...",{
}) |
aout.weibull <-
function(data, param, alpha = 0.1, hide.outliers = FALSE,
lower = auto.l, upper = auto.u, method.in = "Broyden",
global.in = "qline",
control.in = list(sigma = 0.1, maxit = 1000, xtol = 1e-12,
ftol = 1e-12, btol = 1e-4)){
if (!is.numeric(param) | !is.vector(param) | !identical(all.equal(length(param), 2), TRUE))
stop("param must be a numeric vector of length 2.")
if (!is.numeric(data) | !is.vector(data))
stop("data must be a numeric vector.")
if (!identical(all.equal(length(alpha), 1), TRUE) | alpha <= 0 | alpha >= 1)
stop("alpha must be a real number between 0 and 1, but it is ", alpha, ".")
bet <- param[1]
lambda <- param[2]
auto.l <- qweibull(alpha/2, bet, lambda)
auto.u <- qweibull(1 - alpha/2, bet, lambda)
fn3 <- function(x, betta, lammda, a) {
y <- numeric()
y[1] <- 1 - a - pweibull(x[2], betta, lammda) + pweibull(x[1], betta, lammda)
y[2] <- dweibull(x[1], betta, lammda) - dweibull(x[2], betta, lammda)
y
}
if (bet > 1)
{
temp.sol <- nleqslv(c(auto.l, auto.u), fn = fn3, a = alpha, betta = bet,
lammda = lambda, method = method.in, global = global.in,
control = control.in)
if (!identical(all.equal(temp.sol$termcd, 6), TRUE)) temp.region <- temp.sol$x
else {
warning("Lower bound of inlier region is only approximately zero")
temp.region <- c(0, qweibull(1 - alpha, bet, lambda))
}
}
else temp.region <- c(0, qweibull(1 - alpha, bet, lambda))
temp <- data.frame(data = data, is.outlier = (data < temp.region[1] |
data > temp.region[2]))
if (identical(all.equal(hide.outliers, FALSE), TRUE)) temp
else temp[temp[,2] == FALSE, 1]
} |
plot_qqline <- function(data, ycol, group, symsize = 3, symthick = 1, s_alpha = 1, ColPal = "all_grafify", ColSeq = TRUE, ColRev = FALSE, TextXAngle = 0, fontsize = 20, Group, ...){
if (!missing("Group")) {
warning("Use `group` argument instead, as `Group` is deprecated.")
group <- substitute(Group)}
if(missing(group)){
P <- ggplot2::ggplot(data, aes(sample = {{ ycol }}))+
geom_qq_line(na.rm = T,
size = 1,
...)+
geom_qq(na.rm = T,
shape = 21,
fill = "
size = {{ symsize }},
stroke = {{ symthick }},
alpha = {{ s_alpha }},
...)+
theme_classic(base_size = {{ fontsize }})+
theme(strip.background = element_blank())+
guides(x = guide_axis(angle = {{ TextXAngle }}))
} else {
P <- ggplot2::ggplot(data, aes(sample = {{ ycol }},
group = {{ group }}))+
geom_qq_line(na.rm = T,
size = 1,
...)+
geom_qq(na.rm = T,
shape = 21,
aes(fill = {{ group }}),
size = {{ symsize }},
stroke = {{ symthick }},
alpha = {{ s_alpha }},
...)+
labs(fill = enquo(group))+
theme_classic(base_size = {{ fontsize }})+
theme(strip.background = element_blank())+
guides(x = guide_axis(angle = {{ TextXAngle }}))}
if (ColSeq) {
P <- P + scale_fill_grafify(palette = {{ ColPal }}, reverse = {{ ColRev }})
} else {
P <- P + scale_fill_grafify2(palette = {{ ColPal }}, reverse = {{ ColRev }})}
P
} |
lsm_c_contig_mn <- function(landscape, directions = 8) {
landscape <- landscape_as_list(landscape)
result <- lapply(X = landscape,
FUN = lsm_c_contig_mn_calc,
directions = directions)
layer <- rep(seq_along(result),
vapply(result, nrow, FUN.VALUE = integer(1)))
result <- do.call(rbind, result)
tibble::add_column(result, layer, .before = TRUE)
}
lsm_c_contig_mn_calc <- function(landscape, directions) {
contig <- lsm_p_contig_calc(landscape, directions = directions)
if (all(is.na(contig$value))) {
return(tibble::tibble(level = "class",
class = as.integer(NA),
id = as.integer(NA),
metric = "contig_mn",
value = as.double(NA)))
}
contig_mn <- stats::aggregate(x = contig[, 5], by = contig[, 2],
FUN = mean)
return(tibble::tibble(level = "class",
class = as.integer(contig_mn$class),
id = as.integer(NA),
metric = "contig_mn",
value = as.double(contig_mn$value)))
} |
context("detect_entities")
body = get_request_body()
test_that("detect_entities works on single string", {
output <- with_mock(
comprehendHTTP = mock_comprehendHTTP,
detect_entities(text = body$single$Text,
language = body$single$LanguageCode)
)
expected <- read.table(sep="\t", text="
Index BeginOffset EndOffset Score Text Type
0 0 10 0.9999857 Jeff Bezos PERSON
0 23 26 0.6394255 CEO PERSON", header=TRUE, stringsAsFactors=FALSE)
expect_similar(output, expected)
})
test_that("detect_entities works on character vector", {
output <- with_mock(
comprehendHTTP = mock_comprehendHTTP,
detect_entities(text = body$batch$TextList,
language = body$batch$LanguageCode)
)
expected <- read.table(sep="\t", text="
Index BeginOffset EndOffset Score Text Type
0 0 10 0.9999857 Jeff Bezos PERSON
0 23 26 0.6394255 CEO PERSON
2 0 3 0.9972390 AWS ORGANIZATION
2 13 21 0.5615919 numerous QUANTITY", header=TRUE, stringsAsFactors=FALSE)
attr(expected, "ErrorList") <- list()
expect_similar(output, expected)
}) |
DBI <- function (mu.link = "logit", sigma.link = "log")
{
mstats <- checklink("mu.link", "Double Poisson", substitute(mu.link),
c("logit", "probit", "cloglog", "cauchit", "log", "own"))
dstats <- checklink("sigma.link", "Double Poisson", substitute(sigma.link),
c("inverse", "log", "identity", "sqrt"))
structure(
list( family = c("DBI", "Double Binomial"),
parameters = list(mu = TRUE,sigma = TRUE),
nopar = 2,
type = "Discrete",
mu.link = as.character(substitute(mu.link)),
sigma.link = as.character(substitute(sigma.link)),
mu.linkfun = mstats$linkfun,
sigma.linkfun = dstats$linkfun,
mu.linkinv = mstats$linkinv,
sigma.linkinv = dstats$linkinv,
mu.dr = mstats$mu.eta,
sigma.dr = dstats$mu.eta,
dldm = function(y, mu, sigma, bd)
{
y/(mu*sigma)-(bd-y)/((1-mu)*sigma)+as.vector(attr(numeric.deriv(GetBI_C(mu, sigma, bd), "mu"),"gradient"))
},
d2ldm2 = function(y, mu, sigma, bd)
{
dldm <- y/(mu*sigma)-(bd-y)/((1-mu)*sigma)+as.vector(attr(numeric.deriv(GetBI_C(mu, sigma, bd), "mu"),"gradient"))
d2ldm2 <- -dldm^2
d2ldm2 <- ifelse(d2ldm2 < -1e-15, d2ldm2,-1e-15)
d2ldm2
},
dldd = function(y, mu, sigma, bd)
{
logofy <- ifelse(y==0,1,log(y))
logofn_y <- ifelse(bd==y, 1,log(bd-y))
dldd <- (y*logofy)/sigma^2-(log(mu)*y)/sigma^2+(logofn_y*(bd-y))/sigma^2-
(log(1-mu)*(bd-y))/sigma^2-(bd*log(bd))/sigma^2+
as.vector(attr(numeric.deriv(GetBI_C(mu, sigma, bd), "sigma"),"gradient"))
dldd
},
d2ldd2 = function(y, mu, sigma, bd) {
logofy <- ifelse(y==0,1,log(y))
logofn_y <- ifelse(bd==y,1,log(bd-y))
dldd <- (y*logofy)/sigma^2-(log(mu)*y)/sigma^2+(logofn_y* (bd-y))/sigma^2-
(log(1-mu)*(bd-y))/sigma^2-(bd*log(bd))/sigma^2+
as.vector(attr(numeric.deriv(GetBI_C(mu, sigma, bd), "sigma"),"gradient"))
d2ldd2 <- -dldd^2
d2ldd2 <- ifelse(d2ldd2 < -1e-15, d2ldd2,-1e-15)
d2ldd2
},
d2ldmdd = function(y) rep(0,length(y)),
G.dev.incr = function(y, mu, sigma, bd,...) -2*dDBI(y, mu, sigma, bd, log = TRUE),
rqres = expression(
rqres(pfun="pDBI", type="Discrete", ymin=0, bd=bd, y=y, mu=mu, sigma=sigma)
),
mu.initial = expression({mu <- (y + 0.5)/(bd + 1)}),
sigma.initial = expression(sigma <- rep(1.1,length(y))),
mu.valid = function(mu) all(mu > 0) && all(mu < 1),
sigma.valid = function(sigma) all(sigma > 0),
y.valid = function(y) all(y >= 0)
),
class = c("gamlss.family","family"))
}
GetBI_C <- function(mu, sigma, bd)
{
ly <- max(length(bd),length(mu),length(sigma))
bd <- rep(bd, length = ly)
sigma <- rep(1/sigma, length = ly)
mu <- rep(mu, length = ly)
TheC <- .C("getBI_C2",as.double(mu),as.double(sigma),as.double(bd),
as.integer(ly), theC=double(ly))$theC
TheC
}
dDBI <- function(x, mu = .5, sigma = 1, bd=2, log = FALSE)
{
if (any(mu < 0) | any(mu > 1)) stop(paste("mu must be between 0 and 1", "\n", ""))
if (any(x < 0) ) stop(paste("x must be >=0", "\n", ""))
if (any(bd < x)) stop(paste("x must be <= than the binomial denominator", bd, "\n"))
if (any(sigma <= 0)) stop(paste("sigma must be positive", "\n", ""))
if (any(sigma < 1e-10)) warning(" values of sigma in BB less that 1e-10 are set to 1e-10" )
ly <- max(length(x),length(bd),length(mu),length(sigma))
x <- rep(x, length = ly)
bd <- rep(bd, length = ly)
sigma <- rep(sigma, length = ly)
mu <- rep(mu, length = ly)
logofx <- ifelse(x==0,1,log(x))
logofbd_x <- ifelse(bd==x,1,log(bd-x))
res <- GetBI_C(mu,sigma,bd)
ll <- ifelse((abs(sigma-1) < 0.001), dbinom(x, size = bd, prob = mu, log = TRUE),
lchoose(bd,x)+x*logofx+(bd-x)*logofbd_x-bd*log(bd)+
(bd/sigma)*log(bd) + (x/sigma)*log(mu)+((bd-x)/sigma)*log(1-mu)-
(x/sigma)*logofx - ((bd-x)/sigma)*logofbd_x+res)
if(log==FALSE) fy <- exp(ll) else fy <- ll
fy
}
pDBI<-function(q, mu = .5, sigma = 1, bd=2, lower.tail = TRUE, log.p = FALSE)
{
if (any(mu < 0) | any(mu > 1)) stop(paste("mu must be between 0 and 1", "\n", ""))
if (any(sigma <= 0) ) stop(paste("sigma must be greater than 0 ", "\n", ""))
if (any(q < 0) ) stop(paste("q must be >=0", "\n", ""))
ly <- max(length(q),length(mu),length(sigma))
q <- rep(q, length = ly)
sigma <- rep(sigma, length = ly)
mu <- rep(mu, length = ly)
bd <- rep(bd, length = ly)
fn <- function(q, mu, sigma, bd) sum(dDBI(0:q, mu=mu, sigma=sigma, bd=bd))
Vcdf <- Vectorize(fn)
cdf <- Vcdf(q=q, mu=mu, sigma=sigma, bd=bd)
cdf <- if(lower.tail==TRUE) cdf else 1-cdf
cdf <- if(log.p==FALSE) cdf else log(cdf)
cdf
}
qDBI <- function(p, mu = .5, sigma = 1, bd=2, lower.tail = TRUE, log.p = FALSE )
{
if (any(mu < 0) | any(mu > 1)) stop(paste("mu must be between 0 and 1", "\n", ""))
if (any(sigma <= 0) ) stop(paste("sigma must be greater than 0 ", "\n", ""))
if (any(p < 0) | any(p > 1.0001)) stop(paste("p must be between 0 and 1", "\n", ""))
if (log.p==TRUE) p <- exp(p) else p <- p
if (lower.tail==TRUE) p <- p else p <- 1-p
ly <- max(length(p),length(mu),length(sigma))
p <- rep(p, length = ly)
sigma <- rep(sigma, length = ly)
mu <- rep(mu, length = ly)
bd <- rep(bd, length = ly)
QQQ <- rep(0,ly)
nsigma <- rep(sigma, length = ly)
nmu <- rep(mu, length = ly)
for (i in seq(along=p))
{
cumpro <- 0
if (p[i]+0.000000001 >= 1) {QQQ[i] <- bd[i]}
else
{
for (j in seq(from = 0, to = bd[i]))
{
cumpro <- pDBI(j, mu = mu[i], sigma = sigma[i], bd=bd[i], log.p = FALSE)
QQQ[i] <- j
if (p[i] <= cumpro ) break
}
}
}
QQQ
}
rDBI <- function(n,mu = .5, sigma = 1, bd=2)
{
if (any(mu < 0) | any(mu > 1)) stop(paste("mu must be between 0 and 1", "\n", ""))
if (any(sigma <= 0) ) stop(paste("sigma must be greater than 0 ", "\n", ""))
if (any(n <= 0)) stop(paste("n must be a positive integer", "\n", ""))
n <- ceiling(n)
p <- runif(n)
r <- qDBI(p, mu=mu, sigma=sigma, bd = bd )
as.integer(r)
} |
filesroot = "/home/peter/Data/EOD.Global.Indexes"
setwd(paste(filesroot, "/.incoming", sep=""))
start_t<-Sys.time()
if (!file.exists("../.archive")){
dir.create("../.archive", mode="0777")
dir.create("../.archive/csv_files", mode="0777")
dir.create("../.archive/zip_files", mode="0777")
}
if (!file.exists("../.archive/zip_files"))
dir.create("../.archive/zip_files", mode="0777")
if (!file.exists("../.archive/csv_files"))
dir.create("../.archive/csv_files", mode="0777")
zipfiles = list.files(pattern="*.zip")
if(length(zipfiles)>0){
system("unzip \\*.zip")
system("mv *.zip ../.archive/zip_files/")
} else {
print("No zip files to process.")
}
files = list.files(pattern="*.csv")
if(length(files) == 0){
stop("There are no csv files to process in the .incoming directory.")
}
prevfiles = list.files("../.archive/csv_files")
rmfiles = files[files %in% prevfiles]
if(length(rmfiles) >0){
file.remove(rmfiles)
files = files[!files %in% prevfiles]
}
if (length(files) == 0)
stop("There are no files to process or these files have been processed previously. Stopping.")
for (file in files){
print(paste("Splitting ",file,sep=""))
system(paste('awk -F "," ' , "'NR!=1 {filename=$1; sub($1FS,blank); print >> filename",'".csv"',"}'" , file, sep=" "))
system(paste("mv ", file, " ../.archive/csv_files/", file, sep=""))
}
tmpfiles = list.files()
header = 'Date,Open,High,Low,Close,Volume'
for (file in tmpfiles){
targetdir = strtrim(file, (nchar(file)-4))
fullpathdir = paste("../", targetdir, sep="")
fullpathfile = paste("../", targetdir, "/", file, sep="")
if (!file.exists(fullpathdir)){
dir.create(fullpathdir, mode="0777")
system(paste("echo ", header," > ", fullpathfile, sep=""))
}
print(paste("Updating ", file, sep=""))
system(paste("cat ", file, " >> ", fullpathfile, sep=""))
file.remove(file)
}
end_t<-Sys.time()
print(c("Elapsed time: ",end_t-start_t))
print(paste("Processed ", length(files) ," days of prices for ", length(tmpfiles), " symbols.", sep="")) |
dd_logliknorm_rhs2 = function(t,x,m)
{
nx = sqrt(length(x))
dim(x) = c(nx,nx)
xx = matrix(0,nx+2,nx+2)
xx[2:(nx+1),2:(nx+1)] = x
dx = m[[1]] * xx[1:nx,2:(nx+1)] + m[[2]] * xx[3:(nx+2),2:(nx+1)] + m[[4]] * xx[2:(nx+1),1:nx] + m[[5]] * xx[2:(nx+1),3:(nx+2)] - (m[[3]] + m[[6]]) * xx[2:(nx+1),2:(nx+1)]
dim(dx) = c(nx^2,1)
return(list(dx))
} |
ls_fun_calls <- function (x) {
if (is.function(x)) {
c(ls_fun_calls(formals(x)), ls_fun_calls(body(x)))
}
else if (is.call(x)) {
fname <- as.character(x[[1]])
c(list(fname), unlist(lapply(x[-1], ls_fun_calls), use.names = FALSE, recursive = FALSE))
}
} |
NULL
check_i_arguments <- function(arg, nn, n_v, dots = FALSE) {
mc <- match.call()
varname <- sub("dots$", "", deparse(mc[["arg"]]), fixed = TRUE)
if (!is.list(arg)) {
if ((!is.vector(arg, "numeric")) || (length(arg) < 1))
stop(paste(varname, "needs to be a numeric vector of length >= 1!"))
if (dots) {
arg <- as.list(arg)
arg <- lapply(arg, rep, length.out=nn)
} else arg <- lapply(seq_len(n_v), function(x) rep(arg, length.out=nn))
} else {
if (!dots && (length(arg) != n_v))
stop(paste("if", varname, "is a list, its length needs to correspond to the number of accumulators."))
for (i in seq_along(arg)) {
if ((!is.vector(arg[[i]], "numeric")) || (length(arg[[i]]) < 1))
stop(paste0(varname, "[[", i, "]] needs to be a numeric vector of length >= 1!"))
arg[[i]] <- rep(arg[[i]], length.out=nn)
}
}
return(arg)
}
dLBA <- function(rt, response, A, b, t0, ..., st0=0,
distribution = c("norm", "gamma", "frechet", "lnorm"),
args.dist = list(), silent = FALSE) {
dots <- list(...)
if (is.null(names(dots))) stop("... arguments need to be named.")
if (is.data.frame(rt)) {
response <- rt$response
rt <- rt$rt
}
response <- as.numeric(response)
nn <- length(rt)
n_v <- max(vapply(dots, length, 0))
if(!silent)
message(paste("Results based on", n_v, "accumulators/drift rates."))
if (!is.numeric(response) || max(response) > n_v)
stop("response needs to be a numeric vector of integers up to number of accumulators.")
if (any(response < 1))
stop("the first response/accumulator must have value 1.")
if (n_v < 2)
stop("There need to be at least two accumulators/drift rates.")
distribution <- match.arg(distribution)
response <- rep(response, length.out = nn)
A <- check_i_arguments(A, nn=nn, n_v=n_v)
b <- check_i_arguments(b, nn=nn, n_v=n_v)
t0 <- check_i_arguments(t0, nn=nn, n_v=n_v)
switch(distribution,
norm = {
if (any(!(c("mean_v","sd_v") %in% names(dots))))
stop("mean_v and sd_v need to be passed for distribution = \"norm\"")
dots$mean_v <- check_i_arguments(dots$mean_v, nn=nn, n_v=n_v, dots = TRUE)
dots$sd_v <- check_i_arguments(dots$sd_v, nn=nn, n_v=n_v, dots = TRUE)
dots <- dots[c("mean_v","sd_v")]
},
gamma = {
if (!("shape_v" %in% names(dots)))
stop("shape_v needs to be passed for distribution = \"gamma\"")
if ((!("rate_v" %in% names(dots))) & (!("scale_v" %in% names(dots))))
stop("rate_v or scale_v need to be passed for distribution = \"gamma\"")
dots$shape_v <- check_i_arguments(dots$shape_v, nn=nn, n_v=n_v, dots = TRUE)
if ("scale_v" %in% names(dots)) {
dots$scale_v <- check_i_arguments(dots$scale_v, nn=nn, n_v=n_v, dots = TRUE)
if (is.list(dots$scale_v)) {
dots$rate_v <- lapply(dots$scale_v, function(x) 1/x)
} else dots$rate_v <- 1/dots$scale_v
} else dots$rate_v <- check_i_arguments(dots$rate_v, nn=nn, n_v=n_v, dots = TRUE)
dots <- dots[c("shape_v","rate_v")]
},
frechet = {
if (any(!(c("shape_v","scale_v") %in% names(dots))))
stop("shape_v and scale_v need to be passed for distribution = \"frechet\"")
dots$shape_v <- check_i_arguments(dots$shape_v, nn=nn, n_v=n_v, dots = TRUE)
dots$scale_v <- check_i_arguments(dots$scale_v, nn=nn, n_v=n_v, dots = TRUE)
dots <- dots[c("shape_v","scale_v")]
},
lnorm = {
if (any(!(c("meanlog_v","sdlog_v") %in% names(dots))))
stop("meanlog_v and sdlog_v need to be passed for distribution = \"lnorm\"")
dots$meanlog_v <- check_i_arguments(dots$meanlog_v, nn=nn, n_v=n_v, dots = TRUE)
dots$sdlog_v <- check_i_arguments(dots$sdlog_v, nn=nn, n_v=n_v, dots = TRUE)
dots <- dots[c("meanlog_v","sdlog_v")]
}
)
for (i in seq_len(length(dots))) {
if (length(dots[[i]]) < n_v) dots[[i]] <- rep(dots[[i]],length.out=n_v)
}
out <- vector("numeric", nn)
for (i in unique(response)) {
sel <- response == i
out[sel] <- do.call(n1PDF,
args = c(rt=list(rt[sel]),
A = list(lapply(A, "[", i = sel)[c(i, seq_len(n_v)[-i])]),
b = list(lapply(b, "[", i = sel)[c(i, seq_len(n_v)[-i])]),
t0 = list(lapply(t0, "[", i = sel)[c(i, seq_len(n_v)[-i])]),
lapply(dots, function(x)
lapply(x, "[", i = sel)[c(i, seq_len(n_v)[-i])]),
distribution=distribution,
args.dist=list(args.dist),
silent=TRUE, st0 = list(st0)))
}
return(out)
}
pLBA <- function(rt, response, A, b, t0, ..., st0=0,
distribution = c("norm", "gamma", "frechet", "lnorm"),
args.dist = list(), silent = FALSE) {
dots <- list(...)
if (is.null(names(dots)))
stop("... arguments need to be named.")
if (is.data.frame(rt)) {
response <- rt$response
rt <- rt$rt
}
response <- as.numeric(response)
nn <- length(rt)
n_v <- max(vapply(dots, length, 0))
if(!silent) message(paste("Results based on", n_v, "accumulators/drift rates."))
if (!is.numeric(response) || max(response) > n_v)
stop("response needs to be a numeric vector of integers up to number of accumulators.")
if (n_v < 2)
stop("There need to be at least two accumulators/drift rates.")
distribution <- match.arg(distribution)
response <- rep(response, length.out = nn)
A <- check_i_arguments(A, nn=nn, n_v=n_v)
b <- check_i_arguments(b, nn=nn, n_v=n_v)
t0 <- check_i_arguments(t0, nn=nn, n_v=n_v)
switch(distribution,
norm = {
if (any(!(c("mean_v","sd_v") %in% names(dots))))
stop("mean_v and sd_v need to be passed for distribution = \"norm\"")
dots$mean_v <- check_i_arguments(dots$mean_v, nn=nn, n_v=n_v, dots = TRUE)
dots$sd_v <- check_i_arguments(dots$sd_v, nn=nn, n_v=n_v, dots = TRUE)
dots <- dots[c("mean_v","sd_v")]
},
gamma = {
if (!("shape_v" %in% names(dots)))
stop("shape_v needs to be passed for distribution = \"gamma\"")
if ((!("rate_v" %in% names(dots))) & (!("scale_v" %in% names(dots))))
stop("rate_v or scale_v need to be passed for distribution = \"gamma\"")
dots$shape_v <- check_i_arguments(dots$shape_v, nn=nn, n_v=n_v, dots = TRUE)
if ("scale_v" %in% names(dots)) {
dots$scale_v <- check_i_arguments(dots$scale_v, nn=nn, n_v=n_v, dots = TRUE)
if (is.list(dots$scale_v)) {
dots$rate_v <- lapply(dots$scale_v, function(x) 1/x)
} else dots$rate_v <- 1/dots$scale_v
} else dots$rate_v <- check_i_arguments(dots$rate_v, nn=nn, n_v=n_v, dots = TRUE)
dots <- dots[c("shape_v","rate_v")]
},
frechet = {
if (any(!(c("shape_v","scale_v") %in% names(dots))))
stop("shape_v and scale_v need to be passed for distribution = \"frechet\"")
dots$shape_v <- check_i_arguments(dots$shape_v, nn=nn, n_v=n_v, dots = TRUE)
dots$scale_v <- check_i_arguments(dots$scale_v, nn=nn, n_v=n_v, dots = TRUE)
dots <- dots[c("shape_v","scale_v")]
},
lnorm = {
if (any(!(c("meanlog_v","sdlog_v") %in% names(dots))))
stop("meanlog_v and sdlog_v need to be passed for distribution = \"lnorm\"")
dots$meanlog_v <- check_i_arguments(dots$meanlog_v, nn=nn, n_v=n_v, dots = TRUE)
dots$sdlog_v <- check_i_arguments(dots$sdlog_v, nn=nn, n_v=n_v, dots = TRUE)
dots <- dots[c("meanlog_v","sdlog_v")]
}
)
for (i in seq_len(length(dots))) {
if (length(dots[[i]]) < n_v) dots[[i]] <- rep(dots[[i]],length.out=n_v)
}
out <- vector("numeric", nn)
for (i in unique(response)) {
sel <- response == i
out[sel] <- do.call(n1CDF,
args = c(rt=list(rt[sel]),
A = list(lapply(A, "[", i = sel)[c(i, seq_len(n_v)[-i])]),
b = list(lapply(b, "[", i = sel)[c(i, seq_len(n_v)[-i])]),
t0 = list(lapply(t0, "[", i = sel)[c(i, seq_len(n_v)[-i])]),
lapply(dots, function(x)
lapply(x, "[", i = sel)[c(i, seq_len(n_v)[-i])]),
distribution=distribution,
args.dist=list(args.dist),
silent=TRUE, st0 = list(st0)))
}
return(out)
}
inv_cdf_lba <- function(x, response, A, b, t0, ..., st0,
distribution, args.dist, value, abs = TRUE) {
if (abs) abs(value - pLBA(rt=x, response=response,
A=A, b = b, t0 = t0, ..., st0=st0,
distribution=distribution,
args.dist=args.dist,
silent=TRUE))
else (value - pLBA(rt=x, response=response, A=A, b = b, t0 = t0, ..., st0=st0,
distribution=distribution,
args.dist=args.dist,
silent=TRUE))
}
qLBA <- function(p, response, A, b, t0, ..., st0=0,
distribution = c("norm", "gamma", "frechet", "lnorm"),
args.dist = list(), silent = FALSE, interval = c(0, 10),
scale_p = FALSE, scale_max = Inf) {
dots <- list(...)
if (is.null(names(dots))) stop("... arguments need to be named.")
if (is.data.frame(p)) {
response <- p$response
p <- p$p
}
response <- as.numeric(response)
nn <- length(p)
n_v <- max(vapply(dots, length, 0))
if(!silent)
message(paste("Results based on", n_v, "accumulators/drift rates."))
if (!is.numeric(response) || max(response) > n_v)
stop("response needs to be a numeric vector of integers up to number of accumulators.")
if (any(response < 1))
stop("the first response/accumulator must have value 1.")
if (n_v < 2)
stop("There need to be at least two accumulators/drift rates.")
distribution <- match.arg(distribution)
response <- rep(response, length.out = nn)
A <- check_i_arguments(A, nn=nn, n_v=n_v)
b <- check_i_arguments(b, nn=nn, n_v=n_v)
t0 <- check_i_arguments(t0, nn=nn, n_v=n_v)
st0 <- rep(st0, length.out = nn)
out <- vector("numeric", nn)
p <- unname(p)
for (i in seq_len(nn)) {
if (scale_p)
max_p <- do.call(
pLBA,
args = c(
rt = list(scale_max),
response = list(response[i]),
A = ret_arg(A, i),
b = ret_arg(b, i),
t0 = ret_arg(t0, i),
sapply(dots, function(z, i)
sapply(z, ret_arg2, which = i,
simplify = FALSE), i =
i,
simplify = FALSE),
args.dist = list(args.dist),
distribution = distribution,
st0 = list(st0[i]),
silent = TRUE
)
)
else
max_p <- 1
tmp <- do.call(
optimize,
args = c(
f = inv_cdf_lba,
interval = list(interval),
response = list(response[i]),
A = ret_arg(A, i),
b = ret_arg(b, i),
t0 = ret_arg(t0, i),
sapply(dots, function(z, i)
sapply(z, ret_arg2, which = i, simplify = FALSE),
i = i, simplify = FALSE),
args.dist = list(args.dist),
distribution = distribution,
st0 = list(st0[i]),
value = p[i] * max_p,
tol = .Machine$double.eps ^ 0.5
)
)
if (tmp$objective > 0.0001) {
tmp <-
do.call(
optimize,
args = c(
f = inv_cdf_lba,
interval = list(c(min(interval), max(interval) / 2)),
response = list(response[i]),
A = ret_arg(A, i),
b = ret_arg(b, i),
t0 = ret_arg(t0, i),
sapply(dots, function(z, i)
sapply(z, ret_arg2, which = i, simplify = FALSE),
i = i, simplify = FALSE),
args.dist = list(args.dist),
distribution = distribution,
st0 = list(st0[i]),
value = p[i] * max_p,
tol = .Machine$double.eps ^ 0.5
)
)
}
if (tmp$objective > 0.0001) {
try({
uni_tmp <-
do.call(
uniroot,
args = c(
f = inv_cdf_lba,
interval = list(c(min(interval), max(interval) / 2)),
response = list(response[i]),
A = ret_arg(A, i),
b = ret_arg(b, i),
t0 = ret_arg(t0, i),
sapply(dots, function(z, i)
sapply(z, ret_arg2, which = i, simplify = FALSE),
i = i, simplify = FALSE),
args.dist = list(args.dist),
distribution = distribution,
st0 = list(st0[i]),
value = p[i] * max_p,
tol = .Machine$double.eps ^ 0.5,
abs = FALSE
)
)
tmp$objective <- uni_tmp$f.root
tmp$minimum <- uni_tmp$root
}, silent = TRUE)
}
if (tmp$objective > 0.0001) {
tmp[["minimum"]] <- NA
warning(
"Cannot obtain RT that is less than 0.0001 away from desired p = ",
p[i],
".\nIncrease/decrease interval or obtain for different boundary.",
call. = FALSE
)
}
out[i] <- tmp[["minimum"]]
}
return(out
)
}
rLBA <- function(n,A,b,t0, ..., st0=0, distribution = c("norm", "gamma", "frechet", "lnorm"), args.dist = list(), silent = FALSE) {
dots <- list(...)
if (is.null(names(dots))) stop("... arguments need to be named.")
if (any(names(dots) == "")) stop("all ... arguments need to be named.")
n_v <- max(vapply(dots, length, 0))
if(!silent) message(paste("Results based on", n_v, "accumulators/drift rates."))
nn <- n
distribution <- match.arg(distribution)
A <- check_i_arguments(A, nn=nn, n_v=n_v)
b <- check_i_arguments(b, nn=nn, n_v=n_v)
t0 <- check_i_arguments(t0, nn=nn, n_v=n_v)
st0 <- rep(unname(st0), length.out = nn)
switch(distribution,
norm = {
rng <- rlba_norm
if (any(!(c("mean_v","sd_v") %in% names(dots))))
stop("mean_v and sd_v need to be passed for distribution = \"norm\"")
dots$mean_v <- check_i_arguments(dots$mean_v, nn=nn, n_v=n_v, dots = TRUE)
dots$sd_v <- check_i_arguments(dots$sd_v, nn=nn, n_v=n_v, dots = TRUE)
dots <- dots[c("mean_v","sd_v")]
},
gamma = {
rng <- rlba_gamma
if (!("shape_v" %in% names(dots)))
stop("shape_v needs to be passed for distribution = \"gamma\"")
if ((!("rate_v" %in% names(dots))) & (!("scale_v" %in% names(dots))))
stop("rate_v or scale_v need to be passed for distribution = \"gamma\"")
dots$shape_v <- check_i_arguments(dots$shape_v, nn=nn, n_v=n_v, dots = TRUE)
if ("scale_v" %in% names(dots)) {
dots$scale_v <- check_i_arguments(dots$scale_v, nn=nn, n_v=n_v, dots = TRUE)
if (is.list(dots$scale_v)) {
dots$rate_v <- lapply(dots$scale_v, function(x) 1/x)
} else dots$rate_v <- 1/dots$scale_v
} else dots$rate_v <- check_i_arguments(dots$rate_v, nn=nn, n_v=n_v, dots = TRUE)
dots <- dots[c("shape_v","rate_v")]
},
frechet = {
rng <- rlba_frechet
if (any(!(c("shape_v","scale_v") %in% names(dots))))
stop("shape_v and scale_v need to be passed for distribution = \"frechet\"")
dots$shape_v <- check_i_arguments(dots$shape_v, nn=nn, n_v=n_v, dots = TRUE)
dots$scale_v <- check_i_arguments(dots$scale_v, nn=nn, n_v=n_v, dots = TRUE)
dots <- dots[c("shape_v","scale_v")]
},
lnorm = {
rng <- rlba_lnorm
if (any(!(c("meanlog_v","sdlog_v") %in% names(dots))))
stop("meanlog_v and sdlog_v need to be passed for distribution = \"lnorm\"")
dots$meanlog_v <- check_i_arguments(dots$meanlog_v, nn=nn, n_v=n_v, dots = TRUE)
dots$sdlog_v <- check_i_arguments(dots$sdlog_v, nn=nn, n_v=n_v, dots = TRUE)
dots <- dots[c("meanlog_v","sdlog_v")]
}
)
for (i in seq_len(length(dots))) {
if (length(dots[[i]]) < n_v) dots[[i]] <- rep(dots[[i]],length.out=n_v)
}
tmp_acc <- as.data.frame(dots, optional = TRUE)
colnames(tmp_acc) <- sub("\\.c\\(.+", "", colnames(tmp_acc))
parameter_char <- apply(tmp_acc, 1, paste0, collapse = "\t")
parameter_factor <- factor(parameter_char, levels = unique(parameter_char))
parameter_indices <- split(seq_len(nn), f = parameter_factor)
out <- vector("list", length(parameter_indices))
for (i in seq_len(length(parameter_indices))) {
ok_rows <- parameter_indices[[i]]
tmp_dots <- lapply(dots, function(x) sapply(x, "[[", i = ok_rows[1]))
out[[i]] <- do.call(rng,
args = c(n=list(length(ok_rows)),
A = list(sapply(A, "[", i = ok_rows)),
b = list(sapply(b, "[", i = ok_rows)),
t0 = list(sapply(t0, "[", i = ok_rows)),
st0 = list(st0[ok_rows]),
tmp_dots, args.dist))
}
out <- do.call("rbind", out)
as.data.frame(out)
} |
library(testthat)
escapeString <- function(s) {
t <- gsub("(\\\\)", "\\\\\\\\", s)
t <- gsub("(\n)", "\\\\n", t)
t <- gsub("(\r)", "\\\\r", t)
t <- gsub("(\")", "\\\\\"", t)
return(t)
}
prepStr <- function(s) {
t <- escapeString(s)
u <- eval(parse(text=paste0("\"", t, "\"")))
if(s!=u) stop("Unable to escape string!")
t <- paste0("\thtml <- \"", t, "\"")
utils::writeClipboard(t)
return(invisible())
}
evaluationMode <- "sequential"
processingLibrary <- "dplyr"
description <- "test: sequential dplyr"
countFunction <- "n()"
isDevelopmentVersion <- (length(strsplit(packageDescription("pivottabler")$Version, "\\.")[[1]]) > 3)
testScenarios <- function(description="test", releaseEvaluationMode="batch", releaseProcessingLibrary="dplyr", runAllForReleaseVersion=FALSE) {
isDevelopmentVersion <- (length(strsplit(packageDescription("pivottabler")$Version, "\\.")[[1]]) > 3)
if(isDevelopmentVersion||runAllForReleaseVersion) {
evaluationModes <- c("sequential", "batch")
processingLibraries <- c("dplyr", "data.table")
}
else {
evaluationModes <- releaseEvaluationMode
processingLibraries <- releaseProcessingLibrary
}
testCount <- length(evaluationModes)*length(processingLibraries)
c1 <- character(testCount)
c2 <- character(testCount)
c3 <- character(testCount)
c4 <- character(testCount)
testCount <- 0
for(evaluationMode in evaluationModes)
for(processingLibrary in processingLibraries) {
testCount <- testCount + 1
c1[testCount] <- evaluationMode
c2[testCount] <- processingLibrary
c3[testCount] <- paste0(description, ": ", evaluationMode, " ", processingLibrary)
c4[testCount] <- ifelse(processingLibrary=="data.table", ".N", "n()")
}
df <- data.frame(evaluationMode=c1, processingLibrary=c2, description=c3, countFunction=c4, stringsAsFactors=FALSE)
return(df)
}
context("CALCULATION TESTS")
if (requireNamespace("lubridate", quietly = TRUE)) {
scenarios <- testScenarios("calculation tests: calculate dply summarise")
for(i in 1:nrow(scenarios)) {
evaluationMode <- scenarios$evaluationMode[i]
processingLibrary <- scenarios$processingLibrary[i]
description <- scenarios$description[i]
countFunction <- scenarios$countFunction[i]
test_that(description, {
library(pivottabler)
library(dplyr)
library(lubridate)
trains <- mutate(bhmtrains,
ArrivalDelta=difftime(ActualArrival, GbttArrival, units="mins"),
ArrivalDelay=ifelse(ArrivalDelta<0, 0, ArrivalDelta))
pt <- PivotTable$new(processingLibrary=processingLibrary, evaluationMode=evaluationMode,
compatibility=list(totalStyleIsCellStyle=TRUE, explicitHeaderSpansOfOne=TRUE))
pt$addData(trains)
pt$addRowDataGroups("TOC", totalCaption="All TOCs")
pt$defineCalculation(calculationName="TotalTrains", caption="Total Trains",
summariseExpression=countFunction)
pt$defineCalculation(calculationName="MinArrivalDelay", caption="Min Arr. Delay",
summariseExpression="min(ArrivalDelay, na.rm=TRUE)")
pt$defineCalculation(calculationName="MaxArrivalDelay", caption="Max Arr. Delay",
summariseExpression="max(ArrivalDelay, na.rm=TRUE)")
pt$defineCalculation(calculationName="MeanArrivalDelay", caption="Mean Arr. Delay",
summariseExpression="mean(ArrivalDelay, na.rm=TRUE)", format="%.1f")
pt$defineCalculation(calculationName="MedianArrivalDelay", caption="Median Arr. Delay",
summariseExpression="median(ArrivalDelay, na.rm=TRUE)")
pt$defineCalculation(calculationName="IQRArrivalDelay", caption="Delay IQR",
summariseExpression="IQR(ArrivalDelay, na.rm=TRUE)")
pt$defineCalculation(calculationName="SDArrivalDelay", caption="Delay Std. Dev.",
summariseExpression="sd(ArrivalDelay, na.rm=TRUE)", format="%.1f")
pt$evaluatePivot()
html <- "<table class=\"Table\">\n <tr>\n <th class=\"RowHeader\" rowspan=\"1\" colspan=\"1\"> </th>\n <th class=\"ColumnHeader\" colspan=\"1\">Total Trains</th>\n <th class=\"ColumnHeader\" colspan=\"1\">Min Arr. Delay</th>\n <th class=\"ColumnHeader\" colspan=\"1\">Max Arr. Delay</th>\n <th class=\"ColumnHeader\" colspan=\"1\">Mean Arr. Delay</th>\n <th class=\"ColumnHeader\" colspan=\"1\">Median Arr. Delay</th>\n <th class=\"ColumnHeader\" colspan=\"1\">Delay IQR</th>\n <th class=\"ColumnHeader\" colspan=\"1\">Delay Std. Dev.</th>\n </tr>\n <tr>\n <th class=\"RowHeader\" rowspan=\"1\">Arriva Trains Wales</th>\n <td class=\"Cell\">3909</td>\n <td class=\"Cell\">0</td>\n <td class=\"Cell\">49</td>\n <td class=\"Cell\">2.3</td>\n <td class=\"Cell\">1</td>\n <td class=\"Cell\">3</td>\n <td class=\"Cell\">4.3</td>\n </tr>\n <tr>\n <th class=\"RowHeader\" rowspan=\"1\">CrossCountry</th>\n <td class=\"Cell\">22928</td>\n <td class=\"Cell\">0</td>\n <td class=\"Cell\">273</td>\n <td class=\"Cell\">3.5</td>\n <td class=\"Cell\">2</td>\n <td class=\"Cell\">4</td>\n <td class=\"Cell\">8.1</td>\n </tr>\n <tr>\n <th class=\"RowHeader\" rowspan=\"1\">London Midland</th>\n <td class=\"Cell\">48279</td>\n <td class=\"Cell\">0</td>\n <td class=\"Cell\">177</td>\n <td class=\"Cell\">2.3</td>\n <td class=\"Cell\">1</td>\n <td class=\"Cell\">3</td>\n <td class=\"Cell\">4.2</td>\n </tr>\n <tr>\n <th class=\"RowHeader\" rowspan=\"1\">Virgin Trains</th>\n <td class=\"Cell\">8594</td>\n <td class=\"Cell\">0</td>\n <td class=\"Cell\">181</td>\n <td class=\"Cell\">3.0</td>\n <td class=\"Cell\">0</td>\n <td class=\"Cell\">3</td>\n <td class=\"Cell\">8.4</td>\n </tr>\n <tr>\n <th class=\"RowHeader\" rowspan=\"1\">All TOCs</th>\n <td class=\"Cell\">83710</td>\n <td class=\"Cell\">0</td>\n <td class=\"Cell\">273</td>\n <td class=\"Cell\">2.7</td>\n <td class=\"Cell\">1</td>\n <td class=\"Cell\">3</td>\n <td class=\"Cell\">6.1</td>\n </tr>\n</table>"
expect_equal(sum(pt$cells$asMatrix()), 168438.858522)
expect_identical(as.character(pt$getHtml()), html)
})
}
}
if (requireNamespace("lubridate", quietly = TRUE)) {
scenarios <- testScenarios("calculation tests: calculate on rows dply summarise")
for(i in 1:nrow(scenarios)) {
if(!isDevelopmentVersion) break
evaluationMode <- scenarios$evaluationMode[i]
processingLibrary <- scenarios$processingLibrary[i]
description <- scenarios$description[i]
countFunction <- scenarios$countFunction[i]
test_that(description, {
library(pivottabler)
library(dplyr)
library(lubridate)
trains <- mutate(bhmtrains,
ArrivalDelta=difftime(ActualArrival, GbttArrival, units="mins"),
ArrivalDelay=ifelse(ArrivalDelta<0, 0, ArrivalDelta))
pt <- PivotTable$new(processingLibrary=processingLibrary, evaluationMode=evaluationMode,
compatibility=list(totalStyleIsCellStyle=TRUE, explicitHeaderSpansOfOne=TRUE))
pt$addData(trains)
pt$addColumnDataGroups("TOC", totalCaption="All TOCs")
pt$defineCalculation(calculationName="TotalTrains", caption="Total Trains",
summariseExpression=countFunction)
pt$defineCalculation(calculationName="MinArrivalDelay", caption="Min Arr. Delay",
summariseExpression="min(ArrivalDelay, na.rm=TRUE)")
pt$defineCalculation(calculationName="MaxArrivalDelay", caption="Max Arr. Delay",
summariseExpression="max(ArrivalDelay, na.rm=TRUE)")
pt$defineCalculation(calculationName="MeanArrivalDelay", caption="Mean Arr. Delay",
summariseExpression="mean(ArrivalDelay, na.rm=TRUE)", format="%.1f")
pt$defineCalculation(calculationName="MedianArrivalDelay", caption="Median Arr. Delay",
summariseExpression="median(ArrivalDelay, na.rm=TRUE)")
pt$defineCalculation(calculationName="IQRArrivalDelay", caption="Delay IQR",
summariseExpression="IQR(ArrivalDelay, na.rm=TRUE)")
pt$defineCalculation(calculationName="SDArrivalDelay", caption="Delay Std. Dev.",
summariseExpression="sd(ArrivalDelay, na.rm=TRUE)", format="%.1f")
pt$addRowCalculationGroups()
pt$evaluatePivot()
html <- "<table class=\"Table\">\n <tr>\n <th class=\"RowHeader\" rowspan=\"1\" colspan=\"1\"> </th>\n <th class=\"ColumnHeader\" colspan=\"1\">Arriva Trains Wales</th>\n <th class=\"ColumnHeader\" colspan=\"1\">CrossCountry</th>\n <th class=\"ColumnHeader\" colspan=\"1\">London Midland</th>\n <th class=\"ColumnHeader\" colspan=\"1\">Virgin Trains</th>\n <th class=\"ColumnHeader\" colspan=\"1\">All TOCs</th>\n </tr>\n <tr>\n <th class=\"RowHeader\" rowspan=\"1\">Total Trains</th>\n <td class=\"Cell\">3909</td>\n <td class=\"Cell\">22928</td>\n <td class=\"Cell\">48279</td>\n <td class=\"Cell\">8594</td>\n <td class=\"Cell\">83710</td>\n </tr>\n <tr>\n <th class=\"RowHeader\" rowspan=\"1\">Min Arr. Delay</th>\n <td class=\"Cell\">0</td>\n <td class=\"Cell\">0</td>\n <td class=\"Cell\">0</td>\n <td class=\"Cell\">0</td>\n <td class=\"Cell\">0</td>\n </tr>\n <tr>\n <th class=\"RowHeader\" rowspan=\"1\">Max Arr. Delay</th>\n <td class=\"Cell\">49</td>\n <td class=\"Cell\">273</td>\n <td class=\"Cell\">177</td>\n <td class=\"Cell\">181</td>\n <td class=\"Cell\">273</td>\n </tr>\n <tr>\n <th class=\"RowHeader\" rowspan=\"1\">Mean Arr. Delay</th>\n <td class=\"Cell\">2.3</td>\n <td class=\"Cell\">3.5</td>\n <td class=\"Cell\">2.3</td>\n <td class=\"Cell\">3.0</td>\n <td class=\"Cell\">2.7</td>\n </tr>\n <tr>\n <th class=\"RowHeader\" rowspan=\"1\">Median Arr. Delay</th>\n <td class=\"Cell\">1</td>\n <td class=\"Cell\">2</td>\n <td class=\"Cell\">1</td>\n <td class=\"Cell\">0</td>\n <td class=\"Cell\">1</td>\n </tr>\n <tr>\n <th class=\"RowHeader\" rowspan=\"1\">Delay IQR</th>\n <td class=\"Cell\">3</td>\n <td class=\"Cell\">4</td>\n <td class=\"Cell\">3</td>\n <td class=\"Cell\">3</td>\n <td class=\"Cell\">3</td>\n </tr>\n <tr>\n <th class=\"RowHeader\" rowspan=\"1\">Delay Std. Dev.</th>\n <td class=\"Cell\">4.3</td>\n <td class=\"Cell\">8.1</td>\n <td class=\"Cell\">4.2</td>\n <td class=\"Cell\">8.4</td>\n <td class=\"Cell\">6.1</td>\n </tr>\n</table>"
expect_equal(sum(pt$cells$asMatrix()), 168438.858522)
expect_identical(as.character(pt$getHtml()), html)
})
}
}
if (requireNamespace("lubridate", quietly = TRUE)) {
scenarios <- testScenarios("calculation tests: deriving values from other calculations")
for(i in 1:nrow(scenarios)) {
evaluationMode <- scenarios$evaluationMode[i]
processingLibrary <- scenarios$processingLibrary[i]
description <- scenarios$description[i]
countFunction <- scenarios$countFunction[i]
test_that(description, {
library(pivottabler)
library(dplyr)
library(lubridate)
trains <- mutate(bhmtrains,
ArrivalDelta=difftime(ActualArrival, GbttArrival, units="mins"),
ArrivalDelay=ifelse(ArrivalDelta<0, 0, ArrivalDelta),
DelayedByMoreThan5Minutes=ifelse(ArrivalDelay>5,1,0))
pt <- PivotTable$new(processingLibrary=processingLibrary, evaluationMode=evaluationMode,
compatibility=list(totalStyleIsCellStyle=TRUE, explicitHeaderSpansOfOne=TRUE))
pt$addData(trains)
pt$addRowDataGroups("TOC", totalCaption="All TOCs")
pt$defineCalculation(calculationName="DelayedTrains", caption="Trains Arr. 5+ Mins Late",
summariseExpression="sum(DelayedByMoreThan5Minutes, na.rm=TRUE)")
pt$defineCalculation(calculationName="TotalTrains", caption="Total Trains",
summariseExpression=countFunction)
pt$defineCalculation(calculationName="DelayedPercent", caption="% Trains Arr. 5+ Mins Late",
type="calculation", basedOn=c("DelayedTrains", "TotalTrains"),
format="%.1f %%",
calculationExpression="values$DelayedTrains/values$TotalTrains*100")
pt$evaluatePivot()
html <- "<table class=\"Table\">\n <tr>\n <th class=\"RowHeader\" rowspan=\"1\" colspan=\"1\"> </th>\n <th class=\"ColumnHeader\" colspan=\"1\">Trains Arr. 5+ Mins Late</th>\n <th class=\"ColumnHeader\" colspan=\"1\">Total Trains</th>\n <th class=\"ColumnHeader\" colspan=\"1\">% Trains Arr. 5+ Mins Late</th>\n </tr>\n <tr>\n <th class=\"RowHeader\" rowspan=\"1\">Arriva Trains Wales</th>\n <td class=\"Cell\">372</td>\n <td class=\"Cell\">3909</td>\n <td class=\"Cell\">9.5 %</td>\n </tr>\n <tr>\n <th class=\"RowHeader\" rowspan=\"1\">CrossCountry</th>\n <td class=\"Cell\">2780</td>\n <td class=\"Cell\">22928</td>\n <td class=\"Cell\">12.1 %</td>\n </tr>\n <tr>\n <th class=\"RowHeader\" rowspan=\"1\">London Midland</th>\n <td class=\"Cell\">3561</td>\n <td class=\"Cell\">48279</td>\n <td class=\"Cell\">7.4 %</td>\n </tr>\n <tr>\n <th class=\"RowHeader\" rowspan=\"1\">Virgin Trains</th>\n <td class=\"Cell\">770</td>\n <td class=\"Cell\">8594</td>\n <td class=\"Cell\">9.0 %</td>\n </tr>\n <tr>\n <th class=\"RowHeader\" rowspan=\"1\">All TOCs</th>\n <td class=\"Cell\">7483</td>\n <td class=\"Cell\">83710</td>\n <td class=\"Cell\">8.9 %</td>\n </tr>\n</table>"
expect_equal(sum(pt$cells$asMatrix()), 182432.916225)
expect_identical(as.character(pt$getHtml()), html)
})
}
}
scenarios <- testScenarios("calculation tests: showing values only")
for(i in 1:nrow(scenarios)) {
if(!isDevelopmentVersion) break
evaluationMode <- scenarios$evaluationMode[i]
processingLibrary <- scenarios$processingLibrary[i]
description <- scenarios$description[i]
countFunction <- scenarios$countFunction[i]
test_that(description, {
library(pivottabler)
library(dplyr)
trains <- bhmtrains %>%
group_by(TrainCategory, TOC) %>%
summarise(NumberOfTrains=n()) %>%
ungroup()
pt <- PivotTable$new(processingLibrary=processingLibrary, evaluationMode=evaluationMode,
compatibility=list(totalStyleIsCellStyle=TRUE, explicitHeaderSpansOfOne=TRUE))
pt$addData(trains)
pt$addColumnDataGroups("TrainCategory", addTotal=FALSE)
pt$addRowDataGroups("TOC", addTotal=FALSE)
pt$defineCalculation(calculationName="TotalTrains", type="value", valueName="NumberOfTrains")
pt$evaluatePivot()
html <- "<table class=\"Table\">\n <tr>\n <th class=\"RowHeader\" rowspan=\"1\" colspan=\"1\"> </th>\n <th class=\"ColumnHeader\" colspan=\"1\">Express Passenger</th>\n <th class=\"ColumnHeader\" colspan=\"1\">Ordinary Passenger</th>\n </tr>\n <tr>\n <th class=\"RowHeader\" rowspan=\"1\">Arriva Trains Wales</th>\n <td class=\"Cell\">3079</td>\n <td class=\"Cell\">830</td>\n </tr>\n <tr>\n <th class=\"RowHeader\" rowspan=\"1\">CrossCountry</th>\n <td class=\"Cell\">22865</td>\n <td class=\"Cell\">63</td>\n </tr>\n <tr>\n <th class=\"RowHeader\" rowspan=\"1\">London Midland</th>\n <td class=\"Cell\">14487</td>\n <td class=\"Cell\">33792</td>\n </tr>\n <tr>\n <th class=\"RowHeader\" rowspan=\"1\">Virgin Trains</th>\n <td class=\"Cell\">8594</td>\n <td class=\"Cell\"></td>\n </tr>\n</table>"
expect_equal(sum(pt$cells$asMatrix(), na.rm=TRUE), 83710)
expect_identical(as.character(pt$getHtml()), html)
})
}
scenarios <- testScenarios("calculation tests: showing values plus derived totals")
for(i in 1:nrow(scenarios)) {
if(!isDevelopmentVersion) break
evaluationMode <- scenarios$evaluationMode[i]
processingLibrary <- scenarios$processingLibrary[i]
description <- scenarios$description[i]
countFunction <- scenarios$countFunction[i]
test_that(description, {
library(pivottabler)
library(dplyr)
trains <- bhmtrains %>%
group_by(TrainCategory, TOC) %>%
summarise(NumberOfTrains=n()) %>%
ungroup()
pt <- PivotTable$new(processingLibrary=processingLibrary, evaluationMode=evaluationMode,
compatibility=list(totalStyleIsCellStyle=TRUE, explicitHeaderSpansOfOne=TRUE))
pt$addData(trains)
pt$addColumnDataGroups("TrainCategory")
pt$addRowDataGroups("TOC")
pt$defineCalculation(calculationName="TotalTrains",
type="value", valueName="NumberOfTrains",
summariseExpression="sum(NumberOfTrains)")
pt$evaluatePivot()
html <- "<table class=\"Table\">\n <tr>\n <th class=\"RowHeader\" rowspan=\"1\" colspan=\"1\"> </th>\n <th class=\"ColumnHeader\" colspan=\"1\">Express Passenger</th>\n <th class=\"ColumnHeader\" colspan=\"1\">Ordinary Passenger</th>\n <th class=\"ColumnHeader\" colspan=\"1\">Total</th>\n </tr>\n <tr>\n <th class=\"RowHeader\" rowspan=\"1\">Arriva Trains Wales</th>\n <td class=\"Cell\">3079</td>\n <td class=\"Cell\">830</td>\n <td class=\"Cell\">3909</td>\n </tr>\n <tr>\n <th class=\"RowHeader\" rowspan=\"1\">CrossCountry</th>\n <td class=\"Cell\">22865</td>\n <td class=\"Cell\">63</td>\n <td class=\"Cell\">22928</td>\n </tr>\n <tr>\n <th class=\"RowHeader\" rowspan=\"1\">London Midland</th>\n <td class=\"Cell\">14487</td>\n <td class=\"Cell\">33792</td>\n <td class=\"Cell\">48279</td>\n </tr>\n <tr>\n <th class=\"RowHeader\" rowspan=\"1\">Virgin Trains</th>\n <td class=\"Cell\">8594</td>\n <td class=\"Cell\"></td>\n <td class=\"Cell\">8594</td>\n </tr>\n <tr>\n <th class=\"RowHeader\" rowspan=\"1\">Total</th>\n <td class=\"Cell\">49025</td>\n <td class=\"Cell\">34685</td>\n <td class=\"Cell\">83710</td>\n </tr>\n</table>"
expect_equal(sum(pt$cells$asMatrix(), na.rm=TRUE), 334840)
expect_identical(as.character(pt$getHtml()), html)
})
}
scenarios <- testScenarios("calculation tests: showing values plus pre-calculated totals")
for(i in 1:nrow(scenarios)) {
if(!isDevelopmentVersion) break
evaluationMode <- scenarios$evaluationMode[i]
processingLibrary <- scenarios$processingLibrary[i]
description <- scenarios$description[i]
countFunction <- scenarios$countFunction[i]
test_that(description, {
library(dplyr)
library(pivottabler)
trains <- bhmtrains %>%
group_by(TrainCategory, TOC) %>%
summarise(NumberOfTrains=n()) %>%
ungroup()
trainsTrainCat <- bhmtrains %>%
group_by(TrainCategory) %>%
summarise(NumberOfTrains=n()) %>%
ungroup()
trainsTOC <- bhmtrains %>%
group_by(TOC) %>%
summarise(NumberOfTrains=n()) %>%
ungroup()
trainsTotal <- bhmtrains %>%
summarise(NumberOfTrains=n())
pt <- PivotTable$new(processingLibrary=processingLibrary, evaluationMode=evaluationMode)
pt$addData(trains)
pt$addTotalData(trainsTrainCat, variableNames="TrainCategory")
pt$addTotalData(trainsTOC, variableNames="TOC")
pt$addTotalData(trainsTotal, variableNames=NULL)
pt$addColumnDataGroups("TrainCategory")
pt$addRowDataGroups("TOC")
pt$defineCalculation(calculationName="TotalTrains", type="value", valueName="NumberOfTrains")
pt$evaluatePivot()
html <- "<table class=\"Table\">\n <tr>\n <th class=\"RowHeader\"> </th>\n <th class=\"ColumnHeader\">Express Passenger</th>\n <th class=\"ColumnHeader\">Ordinary Passenger</th>\n <th class=\"ColumnHeader\">Total</th>\n </tr>\n <tr>\n <th class=\"RowHeader\">Arriva Trains Wales</th>\n <td class=\"Cell\">3079</td>\n <td class=\"Cell\">830</td>\n <td class=\"Total\">3909</td>\n </tr>\n <tr>\n <th class=\"RowHeader\">CrossCountry</th>\n <td class=\"Cell\">22865</td>\n <td class=\"Cell\">63</td>\n <td class=\"Total\">22928</td>\n </tr>\n <tr>\n <th class=\"RowHeader\">London Midland</th>\n <td class=\"Cell\">14487</td>\n <td class=\"Cell\">33792</td>\n <td class=\"Total\">48279</td>\n </tr>\n <tr>\n <th class=\"RowHeader\">Virgin Trains</th>\n <td class=\"Cell\">8594</td>\n <td class=\"Cell\"></td>\n <td class=\"Total\">8594</td>\n </tr>\n <tr>\n <th class=\"RowHeader\">Total</th>\n <td class=\"Total\">49025</td>\n <td class=\"Total\">34685</td>\n <td class=\"Total\">83710</td>\n </tr>\n</table>"
expect_equal(sum(pt$cells$asMatrix(), na.rm=TRUE), 334840)
expect_identical(as.character(pt$getHtml()), html)
})
}
scenarios <- testScenarios("calculation tests: calcs first 1")
for(i in 1:nrow(scenarios)) {
if(!isDevelopmentVersion) break
evaluationMode <- scenarios$evaluationMode[i]
processingLibrary <- scenarios$processingLibrary[i]
description <- scenarios$description[i]
countFunction <- scenarios$countFunction[i]
test_that(description, {
library(pivottabler)
pt <- PivotTable$new(processingLibrary=processingLibrary, evaluationMode=evaluationMode,
compatibility=list(totalStyleIsCellStyle=TRUE, explicitHeaderSpansOfOne=TRUE))
pt$addData(bhmtrains)
pt$defineCalculation(calculationName="NumberOfTrains", caption="Number of Trains", summariseExpression=countFunction)
pt$defineCalculation(calculationName="MaximumSpeedMPH", caption="Maximum Speed (MPH)", summariseExpression="max(SchedSpeedMPH, na.rm=TRUE)")
pt$addColumnCalculationGroups()
pt$addColumnDataGroups("TrainCategory")
pt$addRowDataGroups("TOC")
pt$evaluatePivot()
html <- "<table class=\"Table\">\n <tr>\n <th class=\"RowHeader\" rowspan=\"2\" colspan=\"1\"> </th>\n <th class=\"ColumnHeader\" colspan=\"3\">Number of Trains</th>\n <th class=\"ColumnHeader\" colspan=\"3\">Maximum Speed (MPH)</th>\n </tr>\n <tr>\n <th class=\"ColumnHeader\" colspan=\"1\">Express Passenger</th>\n <th class=\"ColumnHeader\" colspan=\"1\">Ordinary Passenger</th>\n <th class=\"ColumnHeader\" colspan=\"1\">Total</th>\n <th class=\"ColumnHeader\" colspan=\"1\">Express Passenger</th>\n <th class=\"ColumnHeader\" colspan=\"1\">Ordinary Passenger</th>\n <th class=\"ColumnHeader\" colspan=\"1\">Total</th>\n </tr>\n <tr>\n <th class=\"RowHeader\" rowspan=\"1\">Arriva Trains Wales</th>\n <td class=\"Cell\">3079</td>\n <td class=\"Cell\">830</td>\n <td class=\"Cell\">3909</td>\n <td class=\"Cell\">90</td>\n <td class=\"Cell\">90</td>\n <td class=\"Cell\">90</td>\n </tr>\n <tr>\n <th class=\"RowHeader\" rowspan=\"1\">CrossCountry</th>\n <td class=\"Cell\">22865</td>\n <td class=\"Cell\">63</td>\n <td class=\"Cell\">22928</td>\n <td class=\"Cell\">125</td>\n <td class=\"Cell\">100</td>\n <td class=\"Cell\">125</td>\n </tr>\n <tr>\n <th class=\"RowHeader\" rowspan=\"1\">London Midland</th>\n <td class=\"Cell\">14487</td>\n <td class=\"Cell\">33792</td>\n <td class=\"Cell\">48279</td>\n <td class=\"Cell\">110</td>\n <td class=\"Cell\">100</td>\n <td class=\"Cell\">110</td>\n </tr>\n <tr>\n <th class=\"RowHeader\" rowspan=\"1\">Virgin Trains</th>\n <td class=\"Cell\">8594</td>\n <td class=\"Cell\"></td>\n <td class=\"Cell\">8594</td>\n <td class=\"Cell\">125</td>\n <td class=\"Cell\"></td>\n <td class=\"Cell\">125</td>\n </tr>\n <tr>\n <th class=\"RowHeader\" rowspan=\"1\">Total</th>\n <td class=\"Cell\">49025</td>\n <td class=\"Cell\">34685</td>\n <td class=\"Cell\">83710</td>\n <td class=\"Cell\">125</td>\n <td class=\"Cell\">100</td>\n <td class=\"Cell\">125</td>\n </tr>\n</table>"
expect_equal(sum(pt$cells$asMatrix(), na.rm=TRUE), 336380)
expect_identical(as.character(pt$getHtml()), html)
})
}
scenarios <- testScenarios("calculation tests: calcs first 2")
for(i in 1:nrow(scenarios)) {
if(!isDevelopmentVersion) break
evaluationMode <- scenarios$evaluationMode[i]
processingLibrary <- scenarios$processingLibrary[i]
description <- scenarios$description[i]
countFunction <- scenarios$countFunction[i]
test_that(description, {
library(pivottabler)
pt <- PivotTable$new(processingLibrary=processingLibrary, evaluationMode=evaluationMode,
compatibility=list(totalStyleIsCellStyle=TRUE, explicitHeaderSpansOfOne=TRUE, noDataGroupNBSP=TRUE))
pt$addData(bhmtrains)
pt$addColumnDataGroups("TrainCategory")
pt$addColumnDataGroups("PowerType")
pt$defineCalculation(calculationName="NumberOfTrains", caption="Number of Trains", summariseExpression=countFunction)
pt$defineCalculation(calculationName="MaximumSpeedMPH", caption="Maximum Speed (MPH)", summariseExpression="max(SchedSpeedMPH, na.rm=TRUE)")
pt$addRowCalculationGroups()
pt$addRowDataGroups("TOC")
pt$evaluatePivot()
html <- "<table class=\"Table\">\n <tr>\n <th class=\"RowHeader\" rowspan=\"2\" colspan=\"2\"> </th>\n <th class=\"ColumnHeader\" colspan=\"4\">Express Passenger</th>\n <th class=\"ColumnHeader\" colspan=\"3\">Ordinary Passenger</th>\n <th class=\"ColumnHeader\" colspan=\"1\">Total</th>\n </tr>\n <tr>\n <th class=\"ColumnHeader\" colspan=\"1\">DMU</th>\n <th class=\"ColumnHeader\" colspan=\"1\">EMU</th>\n <th class=\"ColumnHeader\" colspan=\"1\">HST</th>\n <th class=\"ColumnHeader\" colspan=\"1\">Total</th>\n <th class=\"ColumnHeader\" colspan=\"1\">DMU</th>\n <th class=\"ColumnHeader\" colspan=\"1\">EMU</th>\n <th class=\"ColumnHeader\" colspan=\"1\">Total</th>\n <th class=\"ColumnHeader\" colspan=\"1\"></th>\n </tr>\n <tr>\n <th class=\"RowHeader\" rowspan=\"5\">Number of Trains</th>\n <th class=\"RowHeader\" rowspan=\"1\">Arriva Trains Wales</th>\n <td class=\"Cell\">3079</td>\n <td class=\"Cell\"></td>\n <td class=\"Cell\"></td>\n <td class=\"Cell\">3079</td>\n <td class=\"Cell\">830</td>\n <td class=\"Cell\"></td>\n <td class=\"Cell\">830</td>\n <td class=\"Cell\">3909</td>\n </tr>\n <tr>\n <th class=\"RowHeader\" rowspan=\"1\">CrossCountry</th>\n <td class=\"Cell\">22133</td>\n <td class=\"Cell\"></td>\n <td class=\"Cell\">732</td>\n <td class=\"Cell\">22865</td>\n <td class=\"Cell\">63</td>\n <td class=\"Cell\"></td>\n <td class=\"Cell\">63</td>\n <td class=\"Cell\">22928</td>\n </tr>\n <tr>\n <th class=\"RowHeader\" rowspan=\"1\">London Midland</th>\n <td class=\"Cell\">5638</td>\n <td class=\"Cell\">8849</td>\n <td class=\"Cell\"></td>\n <td class=\"Cell\">14487</td>\n <td class=\"Cell\">5591</td>\n <td class=\"Cell\">28201</td>\n <td class=\"Cell\">33792</td>\n <td class=\"Cell\">48279</td>\n </tr>\n <tr>\n <th class=\"RowHeader\" rowspan=\"1\">Virgin Trains</th>\n <td class=\"Cell\">2137</td>\n <td class=\"Cell\">6457</td>\n <td class=\"Cell\"></td>\n <td class=\"Cell\">8594</td>\n <td class=\"Cell\"></td>\n <td class=\"Cell\"></td>\n <td class=\"Cell\"></td>\n <td class=\"Cell\">8594</td>\n </tr>\n <tr>\n <th class=\"RowHeader\" rowspan=\"1\">Total</th>\n <td class=\"Cell\">32987</td>\n <td class=\"Cell\">15306</td>\n <td class=\"Cell\">732</td>\n <td class=\"Cell\">49025</td>\n <td class=\"Cell\">6484</td>\n <td class=\"Cell\">28201</td>\n <td class=\"Cell\">34685</td>\n <td class=\"Cell\">83710</td>\n </tr>\n <tr>\n <th class=\"RowHeader\" rowspan=\"5\">Maximum Speed (MPH)</th>\n <th class=\"RowHeader\" rowspan=\"1\">Arriva Trains Wales</th>\n <td class=\"Cell\">90</td>\n <td class=\"Cell\"></td>\n <td class=\"Cell\"></td>\n <td class=\"Cell\">90</td>\n <td class=\"Cell\">90</td>\n <td class=\"Cell\"></td>\n <td class=\"Cell\">90</td>\n <td class=\"Cell\">90</td>\n </tr>\n <tr>\n <th class=\"RowHeader\" rowspan=\"1\">CrossCountry</th>\n <td class=\"Cell\">125</td>\n <td class=\"Cell\"></td>\n <td class=\"Cell\">125</td>\n <td class=\"Cell\">125</td>\n <td class=\"Cell\">100</td>\n <td class=\"Cell\"></td>\n <td class=\"Cell\">100</td>\n <td class=\"Cell\">125</td>\n </tr>\n <tr>\n <th class=\"RowHeader\" rowspan=\"1\">London Midland</th>\n <td class=\"Cell\">100</td>\n <td class=\"Cell\">110</td>\n <td class=\"Cell\"></td>\n <td class=\"Cell\">110</td>\n <td class=\"Cell\">100</td>\n <td class=\"Cell\">100</td>\n <td class=\"Cell\">100</td>\n <td class=\"Cell\">110</td>\n </tr>\n <tr>\n <th class=\"RowHeader\" rowspan=\"1\">Virgin Trains</th>\n <td class=\"Cell\">125</td>\n <td class=\"Cell\">125</td>\n <td class=\"Cell\"></td>\n <td class=\"Cell\">125</td>\n <td class=\"Cell\"></td>\n <td class=\"Cell\"></td>\n <td class=\"Cell\"></td>\n <td class=\"Cell\">125</td>\n </tr>\n <tr>\n <th class=\"RowHeader\" rowspan=\"1\">Total</th>\n <td class=\"Cell\">125</td>\n <td class=\"Cell\">125</td>\n <td class=\"Cell\">125</td>\n <td class=\"Cell\">125</td>\n <td class=\"Cell\">100</td>\n <td class=\"Cell\">100</td>\n <td class=\"Cell\">100</td>\n <td class=\"Cell\">125</td>\n </tr>\n</table>"
expect_equal(sum(pt$cells$asMatrix(), na.rm=TRUE), 505565)
expect_identical(as.character(pt$getHtml()), html)
})
}
scenarios <- testScenarios("calculation tests: filter overrides - % of row total")
for(i in 1:nrow(scenarios)) {
evaluationMode <- scenarios$evaluationMode[i]
processingLibrary <- scenarios$processingLibrary[i]
description <- scenarios$description[i]
countFunction <- scenarios$countFunction[i]
test_that(description, {
library(pivottabler)
pt <- PivotTable$new(processingLibrary=processingLibrary, evaluationMode=evaluationMode,
compatibility=list(totalStyleIsCellStyle=TRUE, explicitHeaderSpansOfOne=TRUE))
pt$addData(bhmtrains)
pt$addColumnDataGroups("TrainCategory")
pt$addRowDataGroups("TOC")
pt$defineCalculation(calculationName="CountTrains", summariseExpression=countFunction,
caption="Count", visible=FALSE)
filterOverrides <- PivotFilterOverrides$new(pt, keepOnlyFiltersFor="TOC")
pt$defineCalculation(calculationName="TOCTotalTrains", filters=filterOverrides,
summariseExpression=countFunction, caption="TOC Total", visible=FALSE)
pt$defineCalculation(calculationName="PercentageOfTOCTrains", type="calculation",
basedOn=c("CountTrains", "TOCTotalTrains"),
calculationExpression="values$CountTrains/values$TOCTotalTrains*100",
format="%.1f %%", caption="% of TOC")
pt$evaluatePivot()
html <- "<table class=\"Table\">\n <tr>\n <th class=\"RowHeader\" rowspan=\"1\" colspan=\"1\"> </th>\n <th class=\"ColumnHeader\" colspan=\"1\">Express Passenger</th>\n <th class=\"ColumnHeader\" colspan=\"1\">Ordinary Passenger</th>\n <th class=\"ColumnHeader\" colspan=\"1\">Total</th>\n </tr>\n <tr>\n <th class=\"RowHeader\" rowspan=\"1\">Arriva Trains Wales</th>\n <td class=\"Cell\">78.8 %</td>\n <td class=\"Cell\">21.2 %</td>\n <td class=\"Cell\">100.0 %</td>\n </tr>\n <tr>\n <th class=\"RowHeader\" rowspan=\"1\">CrossCountry</th>\n <td class=\"Cell\">99.7 %</td>\n <td class=\"Cell\">0.3 %</td>\n <td class=\"Cell\">100.0 %</td>\n </tr>\n <tr>\n <th class=\"RowHeader\" rowspan=\"1\">London Midland</th>\n <td class=\"Cell\">30.0 %</td>\n <td class=\"Cell\">70.0 %</td>\n <td class=\"Cell\">100.0 %</td>\n </tr>\n <tr>\n <th class=\"RowHeader\" rowspan=\"1\">Virgin Trains</th>\n <td class=\"Cell\">100.0 %</td>\n <td class=\"Cell\"></td>\n <td class=\"Cell\">100.0 %</td>\n </tr>\n <tr>\n <th class=\"RowHeader\" rowspan=\"1\">Total</th>\n <td class=\"Cell\">58.6 %</td>\n <td class=\"Cell\">41.4 %</td>\n <td class=\"Cell\">100.0 %</td>\n </tr>\n</table>"
expect_equal(sum(pt$cells$asMatrix(), na.rm=TRUE), 1000)
expect_identical(as.character(pt$getHtml()), html)
})
}
scenarios <- testScenarios("calculation tests: filter overrides - % of grand total")
for(i in 1:nrow(scenarios)) {
if(!isDevelopmentVersion) break
evaluationMode <- scenarios$evaluationMode[i]
processingLibrary <- scenarios$processingLibrary[i]
description <- scenarios$description[i]
countFunction <- scenarios$countFunction[i]
test_that(description, {
library(pivottabler)
pt <- PivotTable$new(processingLibrary=processingLibrary, evaluationMode=evaluationMode,
compatibility=list(totalStyleIsCellStyle=TRUE, explicitHeaderSpansOfOne=TRUE))
pt$addData(bhmtrains)
pt$addColumnDataGroups("TrainCategory")
pt$addRowDataGroups("TOC")
pt$defineCalculation(calculationName="CountTrains", summariseExpression=countFunction, caption="Count", visible=FALSE)
filterOverrides <- PivotFilterOverrides$new(pt, removeAllFilters=TRUE)
pt$defineCalculation(calculationName="GrandTotalTrains", filters=filterOverrides, summariseExpression=countFunction, caption="Grand Total", visible=FALSE)
pt$defineCalculation(calculationName="PercentageOfAllTrains", type="calculation", basedOn=c("CountTrains", "GrandTotalTrains"),
calculationExpression="values$CountTrains/values$GrandTotalTrains*100", format="%.1f %%", caption="% of All")
pt$evaluatePivot()
html <- "<table class=\"Table\">\n <tr>\n <th class=\"RowHeader\" rowspan=\"1\" colspan=\"1\"> </th>\n <th class=\"ColumnHeader\" colspan=\"1\">Express Passenger</th>\n <th class=\"ColumnHeader\" colspan=\"1\">Ordinary Passenger</th>\n <th class=\"ColumnHeader\" colspan=\"1\">Total</th>\n </tr>\n <tr>\n <th class=\"RowHeader\" rowspan=\"1\">Arriva Trains Wales</th>\n <td class=\"Cell\">3.7 %</td>\n <td class=\"Cell\">1.0 %</td>\n <td class=\"Cell\">4.7 %</td>\n </tr>\n <tr>\n <th class=\"RowHeader\" rowspan=\"1\">CrossCountry</th>\n <td class=\"Cell\">27.3 %</td>\n <td class=\"Cell\">0.1 %</td>\n <td class=\"Cell\">27.4 %</td>\n </tr>\n <tr>\n <th class=\"RowHeader\" rowspan=\"1\">London Midland</th>\n <td class=\"Cell\">17.3 %</td>\n <td class=\"Cell\">40.4 %</td>\n <td class=\"Cell\">57.7 %</td>\n </tr>\n <tr>\n <th class=\"RowHeader\" rowspan=\"1\">Virgin Trains</th>\n <td class=\"Cell\">10.3 %</td>\n <td class=\"Cell\"></td>\n <td class=\"Cell\">10.3 %</td>\n </tr>\n <tr>\n <th class=\"RowHeader\" rowspan=\"1\">Total</th>\n <td class=\"Cell\">58.6 %</td>\n <td class=\"Cell\">41.4 %</td>\n <td class=\"Cell\">100.0 %</td>\n </tr>\n</table>"
expect_equal(sum(pt$cells$asMatrix(), na.rm=TRUE), 400)
expect_identical(as.character(pt$getHtml()), html)
})
}
scenarios <- testScenarios("calculation tests: filter overrides - ratios/multiples")
for(i in 1:nrow(scenarios)) {
if(!isDevelopmentVersion) break
evaluationMode <- scenarios$evaluationMode[i]
processingLibrary <- scenarios$processingLibrary[i]
description <- scenarios$description[i]
countFunction <- scenarios$countFunction[i]
test_that(description, {
library(pivottabler)
pt <- PivotTable$new(processingLibrary=processingLibrary, evaluationMode=evaluationMode,
compatibility=list(totalStyleIsCellStyle=TRUE, explicitHeaderSpansOfOne=TRUE))
pt$addData(bhmtrains)
pt$addColumnDataGroups("TrainCategory")
pt$addRowDataGroups("TOC")
pt$defineCalculation(calculationName="CountTrains", summariseExpression=countFunction, caption="Count", visible=FALSE)
filterOverrides <- PivotFilterOverrides$new(pt, removeAllFilters=TRUE)
filterOverrides$add(variableName="TrainCategory", values="Express Passenger", action="replace")
filterOverrides$add(variableName="TOC", values="CrossCountry", action="replace")
pt$defineCalculation(calculationName="CrossCountryExpress", filters=filterOverrides, summariseExpression=countFunction, caption="CrossCountry Express Trains", visible=FALSE)
pt$defineCalculation(calculationName="MultipleOfCCExpressTrains", type="calculation", basedOn=c("CountTrains", "CrossCountryExpress"),
calculationExpression="values$CountTrains/values$CrossCountryExpress", format="%.2f", caption="Multiple of CC Express")
pt$evaluatePivot()
html <- "<table class=\"Table\">\n <tr>\n <th class=\"RowHeader\" rowspan=\"1\" colspan=\"1\"> </th>\n <th class=\"ColumnHeader\" colspan=\"1\">Express Passenger</th>\n <th class=\"ColumnHeader\" colspan=\"1\">Ordinary Passenger</th>\n <th class=\"ColumnHeader\" colspan=\"1\">Total</th>\n </tr>\n <tr>\n <th class=\"RowHeader\" rowspan=\"1\">Arriva Trains Wales</th>\n <td class=\"Cell\">0.13</td>\n <td class=\"Cell\">0.04</td>\n <td class=\"Cell\">0.17</td>\n </tr>\n <tr>\n <th class=\"RowHeader\" rowspan=\"1\">CrossCountry</th>\n <td class=\"Cell\">1.00</td>\n <td class=\"Cell\">0.00</td>\n <td class=\"Cell\">1.00</td>\n </tr>\n <tr>\n <th class=\"RowHeader\" rowspan=\"1\">London Midland</th>\n <td class=\"Cell\">0.63</td>\n <td class=\"Cell\">1.48</td>\n <td class=\"Cell\">2.11</td>\n </tr>\n <tr>\n <th class=\"RowHeader\" rowspan=\"1\">Virgin Trains</th>\n <td class=\"Cell\">0.38</td>\n <td class=\"Cell\"></td>\n <td class=\"Cell\">0.38</td>\n </tr>\n <tr>\n <th class=\"RowHeader\" rowspan=\"1\">Total</th>\n <td class=\"Cell\">2.14</td>\n <td class=\"Cell\">1.52</td>\n <td class=\"Cell\">3.66</td>\n </tr>\n</table>"
expect_equal(round(sum(pt$cells$asMatrix(), na.rm=TRUE), digits=3), 14.644)
expect_identical(as.character(pt$getHtml()), html)
})
}
scenarios <- testScenarios("calculation tests: filter overrides - subsets of data")
for(i in 1:nrow(scenarios)) {
if(!isDevelopmentVersion) break
evaluationMode <- scenarios$evaluationMode[i]
processingLibrary <- scenarios$processingLibrary[i]
description <- scenarios$description[i]
countFunction <- scenarios$countFunction[i]
test_that(description, {
library(pivottabler)
pt <- PivotTable$new(processingLibrary=processingLibrary, evaluationMode=evaluationMode,
compatibility=list(totalStyleIsCellStyle=TRUE, explicitHeaderSpansOfOne=TRUE))
pt$addData(bhmtrains)
pt$addColumnDataGroups("TrainCategory")
pt$addRowDataGroups("TOC")
filterDMU <- PivotFilter$new(pt, variableName="PowerType", values="DMU")
filterOverrides <- PivotFilterOverrides$new(pt, filter=filterDMU, action="intersect")
pt$defineCalculation(calculationName="CountDMU", filters=filterOverrides, summariseExpression=countFunction, caption="DMU", visible=FALSE)
pt$defineCalculation(calculationName="CountTrains", summariseExpression=countFunction, caption="Count", visible=FALSE)
pt$defineCalculation(calculationName="PercentageDMU", type="calculation", basedOn=c("CountTrains", "CountDMU"),
calculationExpression="values$CountDMU/values$CountTrains*100", format="%.1f %%", caption="% DMU")
pt$evaluatePivot()
html <- "<table class=\"Table\">\n <tr>\n <th class=\"RowHeader\" rowspan=\"1\" colspan=\"1\"> </th>\n <th class=\"ColumnHeader\" colspan=\"1\">Express Passenger</th>\n <th class=\"ColumnHeader\" colspan=\"1\">Ordinary Passenger</th>\n <th class=\"ColumnHeader\" colspan=\"1\">Total</th>\n </tr>\n <tr>\n <th class=\"RowHeader\" rowspan=\"1\">Arriva Trains Wales</th>\n <td class=\"Cell\">100.0 %</td>\n <td class=\"Cell\">100.0 %</td>\n <td class=\"Cell\">100.0 %</td>\n </tr>\n <tr>\n <th class=\"RowHeader\" rowspan=\"1\">CrossCountry</th>\n <td class=\"Cell\">96.8 %</td>\n <td class=\"Cell\">100.0 %</td>\n <td class=\"Cell\">96.8 %</td>\n </tr>\n <tr>\n <th class=\"RowHeader\" rowspan=\"1\">London Midland</th>\n <td class=\"Cell\">38.9 %</td>\n <td class=\"Cell\">16.5 %</td>\n <td class=\"Cell\">23.3 %</td>\n </tr>\n <tr>\n <th class=\"RowHeader\" rowspan=\"1\">Virgin Trains</th>\n <td class=\"Cell\">24.9 %</td>\n <td class=\"Cell\"></td>\n <td class=\"Cell\">24.9 %</td>\n </tr>\n <tr>\n <th class=\"RowHeader\" rowspan=\"1\">Total</th>\n <td class=\"Cell\">67.3 %</td>\n <td class=\"Cell\">18.7 %</td>\n <td class=\"Cell\">47.2 %</td>\n </tr>\n</table>"
expect_equal(round(sum(pt$cells$asMatrix(), na.rm=TRUE), digits=3), 855.192)
expect_identical(as.character(pt$getHtml()), html)
})
}
scenarios <- testScenarios("calculation tests: filter overrides - custom function 1")
for(i in 1:nrow(scenarios)) {
evaluationMode <- scenarios$evaluationMode[i]
processingLibrary <- scenarios$processingLibrary[i]
description <- scenarios$description[i]
countFunction <- scenarios$countFunction[i]
test_that(description, {
library(dplyr)
trains <- bhmtrains %>%
mutate(GbttDateTime=if_else(is.na(GbttArrival), GbttDeparture, GbttArrival),
GbttDate=as.Date(GbttDateTime))
januaryDates <- seq(as.Date("2017-01-01"), as.Date("2017-01-07"), by="days")
getYesterdayDateFilter <- function(pt, filters, cell) {
filter <- filters$getFilter("GbttDate")
if(is.null(filter)||(filter$type=="ALL")||(length(filter$values)>1)) {
newFilter <- PivotFilter$new(pt, variableName="GbttDate", type="NONE")
filters$setFilter(newFilter, action="replace")
}
else {
date <- filter$values
date <- date - 1
filter$values <- date
}
}
library(pivottabler)
pt <- PivotTable$new(processingLibrary=processingLibrary, evaluationMode=evaluationMode,
compatibility=list(totalStyleIsCellStyle=TRUE, explicitHeaderSpansOfOne=TRUE))
pt$addData(trains)
pt$addColumnDataGroups("TrainCategory")
pt$addRowDataGroups("GbttDate", fromData=FALSE,
explicitListOfValues=as.list(januaryDates), visualTotals=TRUE)
pt$defineCalculation(calculationName="CountTrains", summariseExpression=countFunction,
caption="Current Day Count")
filterOverrides <- PivotFilterOverrides$new(pt, overrideFunction=getYesterdayDateFilter)
pt$defineCalculation(calculationName="CountPreviousDayTrains", filters=filterOverrides,
summariseExpression=countFunction, caption="Previous Day Count")
pt$defineCalculation(calculationName="Daily Change", type="calculation",
basedOn=c("CountTrains", "CountPreviousDayTrains"),
calculationExpression="values$CountTrains-values$CountPreviousDayTrains",
caption="Daily Change")
pt$evaluatePivot()
html <- "<table class=\"Table\">\n <tr>\n <th class=\"RowHeader\" rowspan=\"2\" colspan=\"1\"> </th>\n <th class=\"ColumnHeader\" colspan=\"3\">Express Passenger</th>\n <th class=\"ColumnHeader\" colspan=\"3\">Ordinary Passenger</th>\n <th class=\"ColumnHeader\" colspan=\"3\">Total</th>\n </tr>\n <tr>\n <th class=\"ColumnHeader\" colspan=\"1\">Current Day Count</th>\n <th class=\"ColumnHeader\" colspan=\"1\">Previous Day Count</th>\n <th class=\"ColumnHeader\" colspan=\"1\">Daily Change</th>\n <th class=\"ColumnHeader\" colspan=\"1\">Current Day Count</th>\n <th class=\"ColumnHeader\" colspan=\"1\">Previous Day Count</th>\n <th class=\"ColumnHeader\" colspan=\"1\">Daily Change</th>\n <th class=\"ColumnHeader\" colspan=\"1\">Current Day Count</th>\n <th class=\"ColumnHeader\" colspan=\"1\">Previous Day Count</th>\n <th class=\"ColumnHeader\" colspan=\"1\">Daily Change</th>\n </tr>\n <tr>\n <th class=\"RowHeader\" rowspan=\"1\">2017-01-01</th>\n <td class=\"Cell\">297</td>\n <td class=\"Cell\">486</td>\n <td class=\"Cell\">-189</td>\n <td class=\"Cell\">214</td>\n <td class=\"Cell\">309</td>\n <td class=\"Cell\">-95</td>\n <td class=\"Cell\">511</td>\n <td class=\"Cell\">795</td>\n <td class=\"Cell\">-284</td>\n </tr>\n <tr>\n <th class=\"RowHeader\" rowspan=\"1\">2017-01-02</th>\n <td class=\"Cell\">565</td>\n <td class=\"Cell\">297</td>\n <td class=\"Cell\">268</td>\n <td class=\"Cell\">318</td>\n <td class=\"Cell\">214</td>\n <td class=\"Cell\">104</td>\n <td class=\"Cell\">883</td>\n <td class=\"Cell\">511</td>\n <td class=\"Cell\">372</td>\n </tr>\n <tr>\n <th class=\"RowHeader\" rowspan=\"1\">2017-01-03</th>\n <td class=\"Cell\">605</td>\n <td class=\"Cell\">565</td>\n <td class=\"Cell\">40</td>\n <td class=\"Cell\">438</td>\n <td class=\"Cell\">318</td>\n <td class=\"Cell\">120</td>\n <td class=\"Cell\">1043</td>\n <td class=\"Cell\">883</td>\n <td class=\"Cell\">160</td>\n </tr>\n <tr>\n <th class=\"RowHeader\" rowspan=\"1\">2017-01-04</th>\n <td class=\"Cell\">607</td>\n <td class=\"Cell\">605</td>\n <td class=\"Cell\">2</td>\n <td class=\"Cell\">437</td>\n <td class=\"Cell\">438</td>\n <td class=\"Cell\">-1</td>\n <td class=\"Cell\">1044</td>\n <td class=\"Cell\">1043</td>\n <td class=\"Cell\">1</td>\n </tr>\n <tr>\n <th class=\"RowHeader\" rowspan=\"1\">2017-01-05</th>\n <td class=\"Cell\">609</td>\n <td class=\"Cell\">607</td>\n <td class=\"Cell\">2</td>\n <td class=\"Cell\">438</td>\n <td class=\"Cell\">437</td>\n <td class=\"Cell\">1</td>\n <td class=\"Cell\">1047</td>\n <td class=\"Cell\">1044</td>\n <td class=\"Cell\">3</td>\n </tr>\n <tr>\n <th class=\"RowHeader\" rowspan=\"1\">2017-01-06</th>\n <td class=\"Cell\">607</td>\n <td class=\"Cell\">609</td>\n <td class=\"Cell\">-2</td>\n <td class=\"Cell\">436</td>\n <td class=\"Cell\">438</td>\n <td class=\"Cell\">-2</td>\n <td class=\"Cell\">1043</td>\n <td class=\"Cell\">1047</td>\n <td class=\"Cell\">-4</td>\n </tr>\n <tr>\n <th class=\"RowHeader\" rowspan=\"1\">2017-01-07</th>\n <td class=\"Cell\">577</td>\n <td class=\"Cell\">607</td>\n <td class=\"Cell\">-30</td>\n <td class=\"Cell\">433</td>\n <td class=\"Cell\">436</td>\n <td class=\"Cell\">-3</td>\n <td class=\"Cell\">1010</td>\n <td class=\"Cell\">1043</td>\n <td class=\"Cell\">-33</td>\n </tr>\n <tr>\n <th class=\"RowHeader\" rowspan=\"1\">Total</th>\n <td class=\"Cell\">3867</td>\n <td class=\"Cell\"></td>\n <td class=\"Cell\"></td>\n <td class=\"Cell\">2714</td>\n <td class=\"Cell\"></td>\n <td class=\"Cell\"></td>\n <td class=\"Cell\">6581</td>\n <td class=\"Cell\"></td>\n <td class=\"Cell\"></td>\n </tr>\n</table>"
expect_equal(sum(pt$cells$asMatrix(), na.rm=TRUE), 39486)
expect_identical(as.character(pt$getHtml()), html)
})
}
scenarios <- testScenarios("calculation tests: filter overrides - custom function 2")
for(i in 1:nrow(scenarios)) {
if(!isDevelopmentVersion) break
evaluationMode <- scenarios$evaluationMode[i]
processingLibrary <- scenarios$processingLibrary[i]
description <- scenarios$description[i]
countFunction <- scenarios$countFunction[i]
test_that(description, {
library(dplyr)
trains <- bhmtrains %>%
mutate(GbttDateTime=if_else(is.na(GbttArrival), GbttDeparture, GbttArrival),
GbttDate=as.Date(GbttDateTime))
januaryDates <- seq(as.Date("2017-01-01"), as.Date("2017-01-07"), by="days")
getThreeDayFilter <- function(pt, filters, cell) {
filter <- filters$getFilter("GbttDate")
if(is.null(filter)||(filter$type=="ALL")||(length(filter$values)>1)) {
newFilter <- PivotFilter$new(pt, variableName="GbttDate", type="NONE")
filters$setFilter(newFilter, action="replace")
}
else {
date <- filter$values
newDates <- seq(date-1, date+1, by="days")
filter$values <- newDates
}
}
library(pivottabler)
pt <- PivotTable$new(processingLibrary=processingLibrary, evaluationMode=evaluationMode,
compatibility=list(totalStyleIsCellStyle=TRUE, explicitHeaderSpansOfOne=TRUE))
pt$addData(trains)
pt$addColumnDataGroups("TrainCategory")
pt$addRowDataGroups("GbttDate", fromData=FALSE,
explicitListOfValues=as.list(januaryDates), visualTotals=TRUE)
pt$defineCalculation(calculationName="CountTrains", summariseExpression=countFunction,
caption="Current Day Count")
filterOverrides <- PivotFilterOverrides$new(pt, overrideFunction=getThreeDayFilter)
pt$defineCalculation(calculationName="ThreeDayCount", filters=filterOverrides,
summariseExpression=countFunction, caption="Three Day Total")
pt$defineCalculation(calculationName="ThreeDayAverage", type="calculation",
basedOn="ThreeDayCount",
calculationExpression="values$ThreeDayCount/3",
format="%.1f", caption="Three Day Rolling Average")
pt$evaluatePivot()
html <- "<table class=\"Table\">\n <tr>\n <th class=\"RowHeader\" rowspan=\"2\" colspan=\"1\"> </th>\n <th class=\"ColumnHeader\" colspan=\"3\">Express Passenger</th>\n <th class=\"ColumnHeader\" colspan=\"3\">Ordinary Passenger</th>\n <th class=\"ColumnHeader\" colspan=\"3\">Total</th>\n </tr>\n <tr>\n <th class=\"ColumnHeader\" colspan=\"1\">Current Day Count</th>\n <th class=\"ColumnHeader\" colspan=\"1\">Three Day Total</th>\n <th class=\"ColumnHeader\" colspan=\"1\">Three Day Rolling Average</th>\n <th class=\"ColumnHeader\" colspan=\"1\">Current Day Count</th>\n <th class=\"ColumnHeader\" colspan=\"1\">Three Day Total</th>\n <th class=\"ColumnHeader\" colspan=\"1\">Three Day Rolling Average</th>\n <th class=\"ColumnHeader\" colspan=\"1\">Current Day Count</th>\n <th class=\"ColumnHeader\" colspan=\"1\">Three Day Total</th>\n <th class=\"ColumnHeader\" colspan=\"1\">Three Day Rolling Average</th>\n </tr>\n <tr>\n <th class=\"RowHeader\" rowspan=\"1\">2017-01-01</th>\n <td class=\"Cell\">297</td>\n <td class=\"Cell\">1348</td>\n <td class=\"Cell\">449.3</td>\n <td class=\"Cell\">214</td>\n <td class=\"Cell\">841</td>\n <td class=\"Cell\">280.3</td>\n <td class=\"Cell\">511</td>\n <td class=\"Cell\">2189</td>\n <td class=\"Cell\">729.7</td>\n </tr>\n <tr>\n <th class=\"RowHeader\" rowspan=\"1\">2017-01-02</th>\n <td class=\"Cell\">565</td>\n <td class=\"Cell\">1467</td>\n <td class=\"Cell\">489.0</td>\n <td class=\"Cell\">318</td>\n <td class=\"Cell\">970</td>\n <td class=\"Cell\">323.3</td>\n <td class=\"Cell\">883</td>\n <td class=\"Cell\">2437</td>\n <td class=\"Cell\">812.3</td>\n </tr>\n <tr>\n <th class=\"RowHeader\" rowspan=\"1\">2017-01-03</th>\n <td class=\"Cell\">605</td>\n <td class=\"Cell\">1777</td>\n <td class=\"Cell\">592.3</td>\n <td class=\"Cell\">438</td>\n <td class=\"Cell\">1193</td>\n <td class=\"Cell\">397.7</td>\n <td class=\"Cell\">1043</td>\n <td class=\"Cell\">2970</td>\n <td class=\"Cell\">990.0</td>\n </tr>\n <tr>\n <th class=\"RowHeader\" rowspan=\"1\">2017-01-04</th>\n <td class=\"Cell\">607</td>\n <td class=\"Cell\">1821</td>\n <td class=\"Cell\">607.0</td>\n <td class=\"Cell\">437</td>\n <td class=\"Cell\">1313</td>\n <td class=\"Cell\">437.7</td>\n <td class=\"Cell\">1044</td>\n <td class=\"Cell\">3134</td>\n <td class=\"Cell\">1044.7</td>\n </tr>\n <tr>\n <th class=\"RowHeader\" rowspan=\"1\">2017-01-05</th>\n <td class=\"Cell\">609</td>\n <td class=\"Cell\">1823</td>\n <td class=\"Cell\">607.7</td>\n <td class=\"Cell\">438</td>\n <td class=\"Cell\">1311</td>\n <td class=\"Cell\">437.0</td>\n <td class=\"Cell\">1047</td>\n <td class=\"Cell\">3134</td>\n <td class=\"Cell\">1044.7</td>\n </tr>\n <tr>\n <th class=\"RowHeader\" rowspan=\"1\">2017-01-06</th>\n <td class=\"Cell\">607</td>\n <td class=\"Cell\">1793</td>\n <td class=\"Cell\">597.7</td>\n <td class=\"Cell\">436</td>\n <td class=\"Cell\">1307</td>\n <td class=\"Cell\">435.7</td>\n <td class=\"Cell\">1043</td>\n <td class=\"Cell\">3100</td>\n <td class=\"Cell\">1033.3</td>\n </tr>\n <tr>\n <th class=\"RowHeader\" rowspan=\"1\">2017-01-07</th>\n <td class=\"Cell\">577</td>\n <td class=\"Cell\">1503</td>\n <td class=\"Cell\">501.0</td>\n <td class=\"Cell\">433</td>\n <td class=\"Cell\">1083</td>\n <td class=\"Cell\">361.0</td>\n <td class=\"Cell\">1010</td>\n <td class=\"Cell\">2586</td>\n <td class=\"Cell\">862.0</td>\n </tr>\n <tr>\n <th class=\"RowHeader\" rowspan=\"1\">Total</th>\n <td class=\"Cell\">3867</td>\n <td class=\"Cell\"></td>\n <td class=\"Cell\"></td>\n <td class=\"Cell\">2714</td>\n <td class=\"Cell\"></td>\n <td class=\"Cell\"></td>\n <td class=\"Cell\">6581</td>\n <td class=\"Cell\"></td>\n <td class=\"Cell\"></td>\n </tr>\n</table>"
expect_equal(round(sum(pt$cells$asMatrix(), na.rm=TRUE), digits=1), 78457.3)
expect_identical(as.character(pt$getHtml()), html)
})
}
scenarios <- testScenarios("calculation tests: filter overrides - custom function 3")
for(i in 1:nrow(scenarios)) {
if(!isDevelopmentVersion) break
evaluationMode <- scenarios$evaluationMode[i]
processingLibrary <- scenarios$processingLibrary[i]
description <- scenarios$description[i]
countFunction <- scenarios$countFunction[i]
test_that(description, {
library(dplyr)
trains <- bhmtrains %>%
mutate(GbttDateTime=if_else(is.na(GbttArrival), GbttDeparture, GbttArrival),
GbttDate=as.Date(GbttDateTime)) %>%
filter((as.Date("2017-01-01") <= GbttDate)&(GbttDate <= as.Date("2017-01-07")))
januaryDates <- seq(as.Date("2017-01-01"), as.Date("2017-01-07"), by="days")
getCumulativeFilter <- function(pt, filters, cell) {
filter <- filters$getFilter("GbttDate")
if(is.null(filter)||(filter$type=="ALL")||(length(filter$values)>1)) {
}
else {
date <- filter$values
newDates <- seq(as.Date("2017-01-01"), date, by="days")
filter$values <- newDates
}
}
library(pivottabler)
pt <- PivotTable$new(processingLibrary=processingLibrary, evaluationMode=evaluationMode,
compatibility=list(totalStyleIsCellStyle=TRUE, explicitHeaderSpansOfOne=TRUE))
pt$addData(trains)
pt$addColumnDataGroups("TrainCategory")
pt$addRowDataGroups("GbttDate", fromData=FALSE,
explicitListOfValues=as.list(januaryDates))
pt$defineCalculation(calculationName="CountTrains", summariseExpression=countFunction,
caption="Current Day Count")
filterOverrides <- PivotFilterOverrides$new(pt, overrideFunction=getCumulativeFilter)
pt$defineCalculation(calculationName="CumulativeCount", filters=filterOverrides,
summariseExpression=countFunction, caption="Cumulative Count")
pt$evaluatePivot()
html <- "<table class=\"Table\">\n <tr>\n <th class=\"RowHeader\" rowspan=\"2\" colspan=\"1\"> </th>\n <th class=\"ColumnHeader\" colspan=\"2\">Express Passenger</th>\n <th class=\"ColumnHeader\" colspan=\"2\">Ordinary Passenger</th>\n <th class=\"ColumnHeader\" colspan=\"2\">Total</th>\n </tr>\n <tr>\n <th class=\"ColumnHeader\" colspan=\"1\">Current Day Count</th>\n <th class=\"ColumnHeader\" colspan=\"1\">Cumulative Count</th>\n <th class=\"ColumnHeader\" colspan=\"1\">Current Day Count</th>\n <th class=\"ColumnHeader\" colspan=\"1\">Cumulative Count</th>\n <th class=\"ColumnHeader\" colspan=\"1\">Current Day Count</th>\n <th class=\"ColumnHeader\" colspan=\"1\">Cumulative Count</th>\n </tr>\n <tr>\n <th class=\"RowHeader\" rowspan=\"1\">2017-01-01</th>\n <td class=\"Cell\">297</td>\n <td class=\"Cell\">297</td>\n <td class=\"Cell\">214</td>\n <td class=\"Cell\">214</td>\n <td class=\"Cell\">511</td>\n <td class=\"Cell\">511</td>\n </tr>\n <tr>\n <th class=\"RowHeader\" rowspan=\"1\">2017-01-02</th>\n <td class=\"Cell\">565</td>\n <td class=\"Cell\">862</td>\n <td class=\"Cell\">318</td>\n <td class=\"Cell\">532</td>\n <td class=\"Cell\">883</td>\n <td class=\"Cell\">1394</td>\n </tr>\n <tr>\n <th class=\"RowHeader\" rowspan=\"1\">2017-01-03</th>\n <td class=\"Cell\">605</td>\n <td class=\"Cell\">1467</td>\n <td class=\"Cell\">438</td>\n <td class=\"Cell\">970</td>\n <td class=\"Cell\">1043</td>\n <td class=\"Cell\">2437</td>\n </tr>\n <tr>\n <th class=\"RowHeader\" rowspan=\"1\">2017-01-04</th>\n <td class=\"Cell\">607</td>\n <td class=\"Cell\">2074</td>\n <td class=\"Cell\">437</td>\n <td class=\"Cell\">1407</td>\n <td class=\"Cell\">1044</td>\n <td class=\"Cell\">3481</td>\n </tr>\n <tr>\n <th class=\"RowHeader\" rowspan=\"1\">2017-01-05</th>\n <td class=\"Cell\">609</td>\n <td class=\"Cell\">2683</td>\n <td class=\"Cell\">438</td>\n <td class=\"Cell\">1845</td>\n <td class=\"Cell\">1047</td>\n <td class=\"Cell\">4528</td>\n </tr>\n <tr>\n <th class=\"RowHeader\" rowspan=\"1\">2017-01-06</th>\n <td class=\"Cell\">607</td>\n <td class=\"Cell\">3290</td>\n <td class=\"Cell\">436</td>\n <td class=\"Cell\">2281</td>\n <td class=\"Cell\">1043</td>\n <td class=\"Cell\">5571</td>\n </tr>\n <tr>\n <th class=\"RowHeader\" rowspan=\"1\">2017-01-07</th>\n <td class=\"Cell\">577</td>\n <td class=\"Cell\">3867</td>\n <td class=\"Cell\">433</td>\n <td class=\"Cell\">2714</td>\n <td class=\"Cell\">1010</td>\n <td class=\"Cell\">6581</td>\n </tr>\n <tr>\n <th class=\"RowHeader\" rowspan=\"1\">Total</th>\n <td class=\"Cell\">3867</td>\n <td class=\"Cell\">3867</td>\n <td class=\"Cell\">2714</td>\n <td class=\"Cell\">2714</td>\n <td class=\"Cell\">6581</td>\n <td class=\"Cell\">6581</td>\n </tr>\n</table>"
expect_equal(sum(pt$cells$asMatrix(), na.rm=TRUE), 88492)
expect_identical(as.character(pt$getHtml()), html)
})
}
scenarios <- testScenarios("calculation tests: custom function with calcFuncArgs")
for(i in 1:nrow(scenarios)) {
if(!isDevelopmentVersion) break
evaluationMode <- scenarios$evaluationMode[i]
processingLibrary <- scenarios$processingLibrary[i]
description <- scenarios$description[i]
countFunction <- scenarios$countFunction[i]
test_that(description, {
library(pivottabler)
library(dplyr)
library(lubridate)
trains <- mutate(bhmtrains,
GbttDateTime=if_else(is.na(GbttArrival), GbttDeparture, GbttArrival),
GbttDate=make_date(year=year(GbttDateTime), month=month(GbttDateTime), day=day(GbttDateTime)),
GbttMonth=make_date(year=year(GbttDateTime), month=month(GbttDateTime), day=1),
ArrivalDelta=difftime(ActualArrival, GbttArrival, units="mins"),
ArrivalDelay=ifelse(ArrivalDelta<0, 0, ArrivalDelta),
DelayedByMoreThan5Minutes=ifelse(ArrivalDelay>5,1,0))
getWorstSingleDayPerformance <- function(pivotCalculator, netFilters, calcFuncArgs,
format, fmtFuncArgs, baseValues, cell) {
trains <- pivotCalculator$getDataFrame("trains")
filteredTrains <- pivotCalculator$getFilteredDataFrame(trains, netFilters)
dateSummary <- filteredTrains %>%
group_by(GbttDate) %>%
summarise(DelayedPercent = sum(DelayedByMoreThan5Minutes, na.rm=TRUE) / n() * 100) %>%
arrange(desc(DelayedPercent))
tv <- dateSummary$DelayedPercent[1]
date <- dateSummary$GbttDate[1]
if(calcFuncArgs$output=="day") {
value <- list()
value$rawValue <- date
value$formattedValue <- format(date, format="%a %d")
}
else if(calcFuncArgs$output=="performance") {
value <- list()
value$rawValue <- tv
value$formattedValue <- pivotCalculator$formatValue(tv, format=format)
}
return(value)
}
pt <- PivotTable$new(processingLibrary=processingLibrary, evaluationMode=evaluationMode)
pt$addData(trains, "trains")
pt$addColumnDataGroups("GbttMonth", dataFormat=list(format="%B %Y"))
pt$addRowDataGroups("TOC", totalCaption="All TOCs")
pt$defineCalculation(calculationName="WorstSingleDay", caption="Day",
format="%.1f %%", type="function",
calculationFunction=getWorstSingleDayPerformance,
calcFuncArgs=list(output="day"))
pt$defineCalculation(calculationName="WorstSingleDayPerf", caption="Perf",
format="%.1f %%", type="function",
calculationFunction=getWorstSingleDayPerformance,
calcFuncArgs=list(output="performance"))
pt$evaluatePivot()
html <- "<table class=\"Table\">\n <tr>\n <th class=\"RowHeader\" rowspan=\"2\"> </th>\n <th class=\"ColumnHeader\" colspan=\"2\">December 2016</th>\n <th class=\"ColumnHeader\" colspan=\"2\">January 2017</th>\n <th class=\"ColumnHeader\" colspan=\"2\">February 2017</th>\n <th class=\"ColumnHeader\" colspan=\"2\">Total</th>\n </tr>\n <tr>\n <th class=\"ColumnHeader\">Day</th>\n <th class=\"ColumnHeader\">Perf</th>\n <th class=\"ColumnHeader\">Day</th>\n <th class=\"ColumnHeader\">Perf</th>\n <th class=\"ColumnHeader\">Day</th>\n <th class=\"ColumnHeader\">Perf</th>\n <th class=\"ColumnHeader\">Day</th>\n <th class=\"ColumnHeader\">Perf</th>\n </tr>\n <tr>\n <th class=\"RowHeader\">Arriva Trains Wales</th>\n <td class=\"Cell\">Tue 27</td>\n <td class=\"Cell\">42.9 %</td>\n <td class=\"Cell\">Sun 29</td>\n <td class=\"Cell\">18.8 %</td>\n <td class=\"Cell\">Sun 12</td>\n <td class=\"Cell\">18.8 %</td>\n <td class=\"Total\">Tue 27</td>\n <td class=\"Total\">42.9 %</td>\n </tr>\n <tr>\n <th class=\"RowHeader\">CrossCountry</th>\n <td class=\"Cell\">Thu 01</td>\n <td class=\"Cell\">35.4 %</td>\n <td class=\"Cell\">Fri 06</td>\n <td class=\"Cell\">19.4 %</td>\n <td class=\"Cell\">Thu 23</td>\n <td class=\"Cell\">27.9 %</td>\n <td class=\"Total\">Thu 01</td>\n <td class=\"Total\">35.4 %</td>\n </tr>\n <tr>\n <th class=\"RowHeader\">London Midland</th>\n <td class=\"Cell\">Thu 01</td>\n <td class=\"Cell\">26.9 %</td>\n <td class=\"Cell\">Fri 06</td>\n <td class=\"Cell\">17.2 %</td>\n <td class=\"Cell\">Thu 23</td>\n <td class=\"Cell\">12.1 %</td>\n <td class=\"Total\">Thu 01</td>\n <td class=\"Total\">26.9 %</td>\n </tr>\n <tr>\n <th class=\"RowHeader\">Virgin Trains</th>\n <td class=\"Cell\">Thu 01</td>\n <td class=\"Cell\">33.0 %</td>\n <td class=\"Cell\">Thu 12</td>\n <td class=\"Cell\">21.4 %</td>\n <td class=\"Cell\">Sat 11</td>\n <td class=\"Cell\">25.5 %</td>\n <td class=\"Total\">Thu 01</td>\n <td class=\"Total\">33.0 %</td>\n </tr>\n <tr>\n <th class=\"RowHeader\">All TOCs</th>\n <td class=\"Total\">Thu 01</td>\n <td class=\"Total\">29.5 %</td>\n <td class=\"Total\">Fri 06</td>\n <td class=\"Total\">16.3 %</td>\n <td class=\"Total\">Thu 23</td>\n <td class=\"Total\">17.1 %</td>\n <td class=\"Total\">Thu 01</td>\n <td class=\"Total\">29.5 %</td>\n </tr>\n</table>"
expect_equal(round(sum(pt$cells$asMatrix(), na.rm=TRUE), 0), 530)
expect_identical(as.character(pt$getHtml()), html)
})
} |
setMethodS3("extractAlleleSet", "SnpChipEffectSet", function(this, units=NULL, sortUnits=TRUE, transform=log2, ..., verbose=FALSE) {
requireNamespace("oligoClasses") || throw("Package not loaded: oligoClasses")
chipType <- getChipType(this, fullname=FALSE)
cdf <- getCdf(this)
if (is.null(units)) {
units <- indexOf(cdf, pattern="^SNP_A-")
} else {
units <- Arguments$getIndices(units, max=nbrOfUnits(cdf))
}
verbose <- Arguments$getVerbose(verbose)
if (verbose) {
pushState(verbose)
on.exit(popState(verbose))
}
unitNames <- getUnitNames(cdf, units=units)
if (sortUnits) {
verbose && enter(verbose, "Sort units by their names")
srt <- sort(unitNames, method="quick", index.return=TRUE)
unitNames <- srt$x
units <- units[srt$ix]
srt <- NULL;
verbose && exit(verbose)
}
verbose && enter(verbose, "Inferring the number of groups to extract")
verbose && cat(verbose, "Chip type: ", getChipType(this))
ugcMap <- getUnitGroupCellMap(this, units=units, verbose=verbose)
maxGroup <- max(ugcMap$group, na.rm=TRUE)
ugcMap <- NULL
verbose && cat(verbose, "Max number of groups unit (unit,group,cell) map: ", maxGroup)
if (maxGroup > 4) {
maxGroup <- 4L
}
if (!is.element(maxGroup, c(2,4))) {
throw("Unsupported value on 'maxGroup': ", maxGroup)
}
groups <- 1:maxGroup
verbose && cat(verbose, "Groups to extract: ", seqToHumanReadable(groups))
verbose && exit(verbose)
if (maxGroup == 4) {
verbose && enter(verbose, "Inferring which unit groups to be swapped to (sense, antisense)")
dirs <- getGroupDirections(cdf, units=units, verbose=less(verbose, 5))
names(dirs) <- NULL
gc <- gc()
verbose && print(verbose, gc)
lens <- sapply(dirs, FUN=length)
uLens <- unique(lens)
if (any(!is.element(uLens, unique(c(2,maxGroup))))) {
throw("Internal error: Unexpected number of unit groups: ",
paste(uLens, collapse=", "))
}
dirs <- lapply(dirs, FUN=.subset, 1L)
dirs <- unlist(dirs, use.names=FALSE)
idxs <- which(dirs == 2)
dirs <- NULL
gc <- gc()
verbose && print(verbose, gc)
verbose && exit(verbose)
}
verbose && enter(verbose, "Extracting data")
theta <- extractTheta(this, groups=groups, units=units, verbose=verbose)
units <- NULL
gc <- gc()
verbose && print(verbose, gc)
verbose && exit(verbose)
if (maxGroup == 4 && length(idxs) > 0) {
verbose && enter(verbose, "Ordering unit groups to be (sense, antisense)")
verbose && cat(verbose, "Swapping elements:")
verbose && str(verbose, idxs)
theta[idxs,,] <- theta[idxs,c(3,4,1,2),,drop=FALSE]
idxs <- NULL;
gc <- gc()
verbose && print(verbose, gc)
verbose && exit(verbose)
}
if (!is.null(transform)) {
verbose && enter(verbose, "Transforming signals")
theta <- transform(theta)
verbose && str(verbose, theta)
gc <- gc()
verbose && print(verbose, gc)
verbose && exit(verbose)
}
verbose && enter(verbose, "Allocate and populate AlleleSet")
if (maxGroup == 2) {
res <- new("AlleleSet",
alleleA = theta[,1,,drop=TRUE],
alleleB = theta[,2,,drop=TRUE]
)
} else if (maxGroup == 4) {
res <- new("AlleleSet",
antisenseAlleleA = theta[,3,,drop=TRUE],
senseAlleleA = theta[,1,,drop=TRUE],
antisenseAlleleB = theta[,4,,drop=TRUE],
senseAlleleB = theta[,2,,drop=TRUE]
)
}
theta <- NULL
.featureNames(res) <- unitNames
unitNames <- NULL
pdPkgName <- .cleanPlatformName(chipType)
.annotation(res) <- pdPkgName
filenames <- sapply(this, getFilename)
names(filenames) <- NULL
filenames <- gsub(",chipEffects", "", filenames)
.sampleNames(res) <- filenames
verbose && exit(verbose)
res
}) |
test_that("Tests that computing confidence intervals is going well", {
x <- iris[1:125,-1]
y <- iris[1:125,1]
x_test <- iris[126:150,-1]
y_test <- iris[126:150,1]
rf <- forestry(x = x,
y = y,
OOBhonest = TRUE,
seed = 3242)
context("test the bootstrapped prediction intervals")
preds <- getCI(rf, newdata = x_test, level = .99, method = "OOB-bootstrap")
coverage <- length(which(y_test < preds$CI.upper & y_test > preds$CI.lower)) / length(y_test)
skip_if_not_mac()
expect_gt(coverage, 0)
}) |
phf_pbp <- function(game_id = 368719) {
base_url <- "https://web.api.digitalshift.ca/partials/stats/game/play-by-play?game_id="
full_url <- paste0(base_url, game_id)
auth_ticket <- getOption(
"fastRhockey.phf_ticket",
default = 'ticket="4dM1QOOKk-PQTSZxW_zfXnOgbh80dOGK6eUb_MaSl7nUN0_k4LxLMvZyeaYGXQuLyWBOQhY8Q65k6_uwMu6oojuO"'
)
res <- httr::RETRY("GET", full_url,
httr::add_headers(`Authorization`= auth_ticket))
check_status(res)
plays_data <- data.frame()
tryCatch(
expr={
data <- res %>%
httr::content(as = "text", encoding="utf-8") %>%
jsonlite::fromJSON() %>%
purrr::pluck("content") %>%
rvest::read_html() %>%
rvest::html_table()
plays_data <- data[
!sapply(
lapply(data, function(x){
if("Time" %in% colnames(x) && nrow(x)>0){
return(x)
}
if("Play" %in% colnames(x) && nrow(x)>0){
return(x)
}
}),is.null)]
if(length(plays_data) %in% c(5,6)){
plays_data <- plays_data[1:5]
} else if(length(plays_data)>6) {
plays_data
}
plays_df <- purrr::map_dfr(1:length(plays_data), function(x){
plays_data[[x]] %>%
helper_phf_pbp_normalize_columns() %>%
dplyr::mutate(
period_id = x,
game_id = game_id)})
game_details <- phf_game_details(game_id)
plays_df <- plays_df %>%
dplyr::left_join(game_details, by = "game_id")
plays_df <- helper_phf_pbp_data(plays_df)
},
error = function(e) {
message(glue::glue("{Sys.time()}: Invalid game_id or no game data available!"))
},
warning = function(w) {
},
finally = {
}
)
return(plays_df)
} |
mb_get_markets <- function(session_data,event_id,market_states = c("open","suspended"),market_types=c("multirunner","binary"),grading_types=NULL,include_runners=FALSE,include_prices=FALSE)
{
valid_market_states<- c("suspended","open")
valid_market_types <- c("multirunner","binary")
valid_grading_types<- c('asian-handicap','high-score-wins','low-score-wins','point-spread','point-total','single-winner-wins','one_x_two','handicap', 'total', 'both_to_score', 'correct_score', 'half_time_full_time')
content <- list(status_code=0)
if(is.null(session_data)|!is.list(session_data)){
print(paste("You have not provided data about your session in the session_data parameter. Please execute mb_login('my_user_name','verysafepassword') and save the resulting object in a variable e.g. my_session <- mb_login(username,pwd); and pass session_data=my_session as a parameter in this function."));return(content)
}
if(event_id%%1>0){
print(paste("The event_id must be an integer. Please amend and try again."));return(content)
}
if(length(event_id)>1){
print(paste("The event_id must be a single integer. Please amend and try again."));return(content)
}
if(sum(!is.element(market_states,valid_market_states))>0){
print(paste("All market_states values must be one of",paste(valid_market_states,collapse=","),". Please amend and try again."));return(content)
}
if(sum(!is.element(market_types,valid_market_types))>0){
print(paste("All market_types values must be one of",paste(valid_market_types,collapse=","),". Please amend and try again."));return(content)
}
if(sum(!is.null(grading_types))>0)
{
if(sum(!is.element(grading_types,valid_grading_types))>0){
print(paste("All grading_types values must be one of",paste(valid_grading_types,collapse=","),". Please amend and try again."));return(content)
}
}
param_list <- list('exchange-type'='back-lay','odds-type'=session_data$odds_type,currency=session_data$currency,'market-states'=paste(market_states,collapse=","),'per-page'='500','types'=paste(grading_types,collapse=","),'market-types'=paste(market_types,collapse=","))
if(include_runners==TRUE){
param_list <- c(param_list,'include-runners'='true')
}
if(include_prices==TRUE){
param_list <- c(param_list,'include-prices'='true')
}
get_markets_resp <- httr::GET(paste("https://www.matchbook.com/edge/rest/events/",event_id,"/markets",sep=""),query=param_list,httr::set_cookies('session-token'=session_data$session_token),httr::add_headers('User-Agent'='rlibnf'))
status_code <- get_markets_resp$status_code
if(status_code==200)
{
content <- jsonlite::fromJSON(content(get_markets_resp, "text", "application/json"))$markets
} else
{
print(paste("Warning/Error in communicating with https://www.matchbook.com/edge/rest/events/",event_id,"/markets",sep=""))
content$status_code <- status_code
}
return(content)
} |
getParseFun <- function(parseData)
{
UPROWS <- 4L
w <- which(parseData$token == "FUNCTION")
w <- w[getParseGGParent(parseData, parseData$id[w]) == 0L]
parseData$text[w - UPROWS]
}
getParseFormals <- function(parseData)
{
w <- which(parseData$token == "SYMBOL_FORMALS")
w <- w[getParseGGParent(parseData, parseData$id[w]) == 0L]
x <- split(parseData$text[w], parseData$parent[w])
names(x) <- NULL
x
} |
findOOBErrors <- function(forest, X.train, Y.train = NULL, n.cores = 1) {
categorical <- grepl("class", c(forest$type, forest$family, forest$treetype), TRUE)
if ("quantregForest" %in% class(forest)) {
class(forest) <- "randomForest"
train.terminal.nodes <- attr(predict(forest, X.train, nodes = TRUE), "nodes")
bag.count <- forest$inbag
if (categorical) {
oob.errors <- forest$y != forest$predicted
} else {
oob.errors <- forest$y - forest$predicted
}
} else if ("randomForest" %in% class(forest)) {
train.terminal.nodes <- attr(predict(forest, X.train, nodes = TRUE), "nodes")
bag.count <- forest$inbag
if (categorical) {
oob.errors <- forest$y != forest$predicted
} else {
oob.errors <- forest$y - forest$predicted
}
} else if ("ranger" %in% class(forest)) {
train.terminal.nodes <- predict(forest, X.train, num.threads = n.cores, type = "terminalNodes")$predictions
bag.count <- matrix(unlist(forest$inbag.counts, use.names = FALSE), ncol = forest$num.trees, byrow = FALSE)
if (categorical) {
oob.errors <- Y.train != forest$predictions
} else {
oob.errors <- Y.train - forest$predictions
}
} else if ("rfsrc" %in% class(forest)) {
train.terminal.nodes <- forest$membership
bag.count <- forest$inbag
if (categorical) {
oob.errors <- forest$yvar != forest$class.oob
} else {
oob.errors <- forest$yvar - forest$predicted.oob
}
}
train.terminal.nodes[bag.count != 0] <- NA
train_nodes <- data.table::as.data.table(train.terminal.nodes)
train_nodes[, `:=`(oob_error = oob.errors)]
train_nodes <- data.table::melt(
train_nodes,
id.vars = c("oob_error"),
measure.vars = 1:ncol(train.terminal.nodes),
variable.name = "tree",
value.name = "terminal_node",
variable.factor = FALSE,
na.rm = TRUE)
train_nodes <- train_nodes[,
.(node_errs = list(oob_error)),
keyby = c("tree", "terminal_node")]
return(train_nodes)
} |
PRMR <- function (Data.Name, JK = NULL, CL = NULL,
PeriodEnd = NULL, Period = NULL)
{
v013 <- rweight <- mm1 <- age5 <- birth <- exposure <- sex <- agegrp <- mm_death <- NULL
Data.Name <- Data.Name[!Data.Name$v005 == 0, ]
Data.Name$ID <- seq.int(nrow(Data.Name))
if (is.null(CL)) {
Z <- stats::qnorm(.025,lower.tail=FALSE)
} else {
Z <- stats::qnorm((100-CL)/200,lower.tail=FALSE)
}
if (is.null(Period)){Periodmsg = 84} else {Periodmsg = Period}
if (is.null(PeriodEnd)){
PeriodEndy_ <- as.integer((mean(Data.Name$v008) - 1)/12)+1900
PeriodEndm_ <- round(mean(Data.Name$v008) - ((PeriodEndy_ - 1900) * 12),0)
PeriodEndm_m <- round(min(Data.Name$v008) - ((PeriodEndy_ - 1900) * 12),0)
PeriodEndm_x <- round(max(Data.Name$v008) - ((PeriodEndy_ - 1900) * 12),0)
} else {
dates <- paste(PeriodEnd, "01", sep = "-")
PeriodEndm_ <- as.numeric(format(as.Date(dates), "%m"))
PeriodEndy_ <- as.numeric(format(as.Date(dates), "%Y"))
if (PeriodEndm_ >= round(mean(Data.Name$v008) - (((as.integer((mean(Data.Name$v008) - 1)/12)+1900) - 1900) * 12),0) &
PeriodEndy_ >= as.integer((mean(Data.Name$v008) - 1)/12)+1900)
message(crayon::bold("Note:", "\n",
"You specified a reference period that ends after the survey fieldwork dates....."), "\n",
"1. Make sure the dates in the survey are coded according to the Gregorian calendar.", "\n",
"2. If the dates are coded according to the Gregorian calendar, use a proper PeriodEnd that came before the time of the survey.", "\n",
"3. If the dates are not coded according to the Gregorian calendar, use a PeriodEnd according to the used calendar.")
}
if (is.null(PeriodEnd)){
cat("\n", crayon::white$bgBlue$bold("The current function calculated PRMR based on a reference period of"),
crayon::red$bold$underline(Periodmsg), crayon::white$bold$bgBlue("months"), "\n", crayon::white$bold$bgBlue("The reference period ended at the time of the interview, in"), crayon::red$bold$underline(PeriodEndy_ + round(PeriodEndm_/12,digits=2)), "OR", crayon::red$bold$underline(month.abb[PeriodEndm_m]), "-", crayon::red$bold$underline(month.abb[PeriodEndm_x]), crayon::red$bold$underline(PeriodEndy_), "\n",
crayon::white$bold$bgBlue("The average reference period is"), crayon::red$bold$underline(round((PeriodEndy_ + PeriodEndm_/12)-(Periodmsg/24), digits =2)), "\n")
} else {
cat("\n", crayon::white$bgBlue$bold("The current function calculated PRMR based on a reference period of"),
crayon::red$bold$underline(Periodmsg), crayon::white$bold$bgBlue("months"), "\n", crayon::white$bold$bgBlue("The reference period ended in"), crayon::red$bold$underline(PeriodEndy_ + round(PeriodEndm_/12,digits=2)), "OR", crayon::red$bold$underline(month.abb[PeriodEndm_]), crayon::red$bold$underline(PeriodEndy_), "\n",
crayon::white$bold$bgBlue("The average reference period is"), crayon::red$bold$underline(round((PeriodEndy_ + PeriodEndm_/12)-(Periodmsg/24), digits =2)), "\n")
}
Data.Name$id <- c(as.factor(Data.Name$v021))
Data.Name$rweight = Data.Name$v005 / 1000000
DeathEx <- DataPrepareM(Data.Name, PeriodEnd, Period)
BirthEx <- DataPrepareM_GFR(Data.Name, PeriodEnd, Period)
if (is.null(JK)){PSU <- 0} else {PSU <- max(as.numeric(DeathEx$id))}
DeathEx$mm9[is.na(DeathEx$mm9)] <- 0
DeathEx$prm_death = ifelse(DeathEx$mm1 ==2 & DeathEx$mm9 >= 2 & DeathEx$mm9 <= 6, DeathEx$death, 0)
DeathEx$mm_death = DeathEx$prm_death
options(dplyr.summarise.inform = FALSE)
AGEDIST <- (dplyr::group_by(Data.Name, v013) %>% summarise(x = sum(rweight)))$x/sum(Data.Name$rweight)
options(survey.lonely.psu = "adjust")
dstrat <- survey::svydesign(id = ~ v021, strata = ~ v022, weights = ~ rweight, data = DeathEx)
dsub <- subset(dstrat, mm1==2)
asprmr <- (survey::svyby(~ mm_death, by = ~ agegrp, denominator = ~ exposure,
design = dsub, survey::svyratio))[, 2]
ASFR <- (dplyr::group_by(BirthEx, age5) %>% summarise(x = sum(birth*rweight)))$x/
(dplyr::group_by(BirthEx, age5) %>% summarise(x = sum(exposure*rweight)))$x
prmr <- (sum(asprmr[1:7] * AGEDIST) * 100000) / ceiling(sum(ASFR[1:7] * AGEDIST))
N = (dplyr::group_by(DeathEx, sex) %>% summarise(x = sum(exposure)))$x[1]
WN = (survey::svyby(~ exposure, by = ~ sex, design = dsub, survey::svytotal))$exposure[1]
PRMR_DEFT = sqrt(survey::svyby(~ mm_death, by = ~ sex, denominator = ~ exposure,
design = dsub, deff = "replace", survey::svyratio)$DEff)[1]
JKres <- matrix(0, nrow = PSU, ncol = 1)
dimnames(JKres) <- list(NULL, c("PRMRj_f"))
if (is.null(JK)){
RESULTS <- cbind.data.frame(round(WN, 0),round(prmr, 0), round(N, 0), row.names = NULL)
names(RESULTS) <- c("Exposure_years","PRMR", "N")
list(RESULTS)
} else {
for (i in unique(as.numeric(DeathEx$id)))
{
Data.NameJ <- Data.Name[which(!Data.Name$id == i), ]
DeathExJ <- DeathEx[which(!DeathEx$id == i), ]
BirthExJ <- BirthEx[which(!BirthEx$id == i), ]
AGEDISTj <- (dplyr::group_by(Data.NameJ, v013) %>% summarise(x = sum(rweight)))$x/sum(Data.NameJ$rweight)
ASPRMRj <- (dplyr::group_by(DeathExJ, sex, agegrp) %>% summarise(x = sum(mm_death*rweight)))$x/
(dplyr::group_by(DeathExJ, sex, agegrp) %>% summarise(x = sum(exposure*rweight)))$x
ASFRj <- (dplyr::group_by(BirthExJ, age5) %>% summarise(x = sum(birth*rweight)))$x/
(dplyr::group_by(BirthExJ, age5) %>% summarise(x = sum(exposure*rweight)))$x
JKres[i,1] <- (sum(ASPRMRj[1:7] * AGEDISTj) * 100000) / (sum(ASFRj[1:7] * AGEDISTj))
}
PRMRjf = JKres[1:PSU, 1]
JKSEf = ((PSU * prmr[1] - (PSU-1) * PRMRjf)-prmr[1])^2
SE = sqrt(sum(JKSEf) / (PSU * (PSU-1)))
RSE = SE / prmr
LCI = prmr - (Z * SE)
LCI[LCI <= 0] = 0
UCI = prmr + (Z * SE)
PSUs = PSU
RESULTS <- cbind.data.frame(round(WN, 0), round(prmr,0), round(SE,3), round(N, 0), round(PRMR_DEFT,3), round(RSE,3), round(LCI,3), round(UCI,3), PSUs, row.names = NULL)
names(RESULTS) <- c("Exposure_years","PRMR", "SE", "N", "DEFT", "RSE", "LCI", "UCI", "iterations")
list(RESULTS)
}
} |
genlabels <- function(X) {
if(is.vector(X)) {
if(length(X)==3) names(X) <- c("AA","AB","BB")
if(length(X)==5) names(X) <- c("A","B","AA","AB","BB")
}
if(is.matrix(X) | is.data.frame(X)) {
if(ncol(X)==3) colnames(X) <- c("AA","AB","BB")
if(ncol(X)==5) colnames(X) <- c("A","B","AA","AB","BB")
}
return(X)
} |
prcbr <- function (formula, b0, data, logL = TRUE, omethod = "BFGS", lo = -Inf,
up = Inf, ...)
{
mf <- match.call(expand.dots = FALSE)
m <- match(c("formula", "data"), names(mf), 0)
mf <- mf[c(1, m)]
F <- Formula::Formula(formula)
mf[[1]] <- as.name("model.frame")
mf$formula <- F
mf <- eval(mf, parent.frame())
y <- model.response(mf)
X <- model.matrix(F, data = mf, rhs = 1)
Z <- model.matrix(F, data = mf, rhs = 2)[, -1, drop = FALSE]
p <- NCOL(Z)
b0 <- matrix(b0, p)
M <- ncol(b0)
cntl <- KW.control(...)
eps <- .Machine$double.eps^(2/3)
plogL <- function(b, X, y, Z, cntl) {
-logLik(rcbr.fit(X, y, offset = Z %*% b, mode = "KW",
cntl))
}
pglogL <- function(b, X, y, Z, cntl) {
f <- rcbr.fit(X, y, offset = Z %*% b, mode = "KW", cntl)
if (class(f)[[1]] == "KW1") {
Fhat <- stepfun(f$x, cumsum(c(0, f$y)))
FZb <- Fhat(X[, 2] + Z %*% b)
}
else if (class(f)[[1]] == "KW2") {
Fhatu <- stepfun(f$uv[, 1], cumsum(c(0, f$W)))
Fhatv <- stepfun(f$uv[, 2], cumsum(c(0, f$W)))
Fhat <- function(u, v) pmin(Fhatu(u), Fhatv(v))
FZb <- Fhat(X[, 3], X[, 2] + Z %*% b)
}
FZb <- FZb * ((eps < FZb) && (FZb < (1 - eps)))
c(crossprod(crossprod(Z, y - FZb)/length(y)))
}
if (logL) {
if (M == 1) {
g <- optim(b0, plogL, method = omethod, X = X, y = y, Z = Z,
lower = lo, upper = up, cntl = cntl)
bhat <- g$par
}
else {
g <- apply(b0, 2, FUN = function(b) plogL(b, X, y,
Z, cntl = cntl))
bhat <- b0[, which(g == max(g))]
}
f <- rcbr.fit(X, y, offset = Z %*% bhat, mode = "KW",
cntl)
}
else {
if (M == 1) {
g <- optim(b0, pglogL, method = omethod, X = X, y = y, Z = Z,
lower = lo, upper = up, cntl = cntl)
bhat <- g$par
}
else {
g <- apply(b0, 2, FUN = function(b) pglogL(b, X,
y, Z, cntl = cntl))
bhat <- b0[, which(g == min(g))]
}
f <- rcbr.fit(X, y, offset = Z %*% bhat, mode = "KW",
cntl)
}
list(bopt = g, fopt = f)
} |
convertBMRToRankMatrix = function(bmr, measure = NULL, ties.method = "average", aggregation = "default") {
assertClass(bmr, "BenchmarkResult")
measure = checkBMRMeasure(measure, bmr)
assertChoice(aggregation, c("mean", "default"))
if (aggregation == "mean") {
df = setDT(as.data.frame(bmr))
df = df[, list(x = mean(get(measure$id))), by = c("task.id", "learner.id")]
} else if (aggregation == "default") {
aggr.meas = measureAggrName(measure)
df = setDT(getBMRAggrPerformances(bmr, as.df = TRUE))
df = df[, c("task.id", "learner.id", aggr.meas), with = FALSE]
setnames(df, aggr.meas, "x")
}
if (!measure$minimize) {
df$x = -df$x
}
df[, "alg.rank" := rank(.SD$x, ties.method = ties.method), by = "task.id"]
df = melt(df, c("task.id", "learner.id"), "alg.rank")
df = dcast(df, learner.id ~ task.id)
task.id.names = setdiff(colnames(df), "learner.id")
mat = as.matrix(setDF(df)[, task.id.names])
rownames(mat) = df$learner.id
colnames(mat) = task.id.names
return(mat)
} |
lavMultipleImputation <-
function(model = NULL,
dataList = NULL,
ndat = length(dataList),
cmd = "sem",
...,
store.slots = c("partable"),
FUN = NULL,
show.progress = FALSE,
parallel = c("no", "multicore", "snow"),
ncpus = max(1L, parallel::detectCores() - 1L),
cl = NULL) {
dotdotdot <- list()
fit <- do.call("lavaanList", args = c(list(model = model,
dataList = dataList, ndat = ndat, cmd = cmd,
store.slots = store.slots, FUN = FUN,
show.progress = show.progress,
parallel = parallel, ncpus = ncpus, cl = cl), dotdotdot))
fit@meta$lavMultipleImputation <- TRUE
fit
} |
print.spTD<-function(x, ...) {
cat("-----------------------------------------------------"); cat('\n');
cat("Model: "); cat(x$model); cat('\n');
cat("Call: "); print(x$call);
cat("Iterations: "); cat(x$iterations); cat("\n")
cat("nBurn: "); cat(x$nBurn); cat("\n")
cat("Acceptance rate for phi (%): "); cat(x$accept); cat("\n")
cat("-----------------------------------------------------"); cat('\n');
print(x$PMCC);
cat("-----------------------------------------------------"); cat('\n');
cat("Computation time: "); cat(x$computation.time); cat("\n")
}
fitted.spTD<-function(object, ...){
x<-data.frame(object$fitted)
x
}
confint.spTD<-function(object, parm, level=0.95, ...){
x<-as.mcmc(object)
up<-level+(1-level)/2
low<-(1-level)/2
FUN <- function(x){quantile(x,probs=c(low,up))}
out<-apply(x,2,FUN=FUN)
out<-t(out)
if(missing(parm)){
out
}
else{
if(length(parm)>1){
out<-as.matrix(out[dimnames(out)[[1]] %in% parm,])
out
}
else{
out<-t(as.matrix(out[dimnames(out)[[1]] %in% parm,]))
dimnames(out)[[1]]<-parm
out
}
}
}
coef.spTD<-function(object, digits=4, ...){
round(t(object$parameter)[1,],digits=digits)
}
residuals.spTD<-function(object, ...){
if(object$scale.transform=="NONE"){
tmp<-object$Y-object$fitted[,1]
tmp
}
else if(object$scale.transform=="SQRT"){
tmp<-sqrt(object$Y)-object$fitted[,1]
tmp
}
else if(object$scale.transform=="LOG"){
tmp<-log(object$Y)-object$fitted[,1]
tmp
}
else{
}
}
as.mcmc.spTD<-function(x, ...){
if (x$combined.fit.pred == TRUE) {
stop("\n
}
model <- x$model
if (is.null(model) == TRUE) {
stop("\n
}
else if (model == "GP" | model == "truncatedGP") {
r <- x$r
p <- x$p
if(x$cov.fnc=="matern"){
if((is.null(x$sp.covariate.names)) & (is.null(x$tp.covariate.names))){
para <- rbind((x$betap), t(x$sig2ep), t(x$sig2etap), t(x$phip), t(x$nup))
dimnames(para)[[1]] <- c(dimnames(x$X)[[2]], "sig2eps", "sig2eta", "phi", "nu")
}
else if((!is.null(x$sp.covariate.names)) & (is.null(x$tp.covariate.names))){
para <- rbind((x$betap), t(x$sig2ep), t(x$sig2etap), t(x$sig2betap), t(x$phip), t(x$nup))
dimnames(para)[[1]] <- c(dimnames(x$X)[[2]], "sig2eps", "sig2eta", "sig2beta", "phi", "nu")
}
else if((is.null(x$sp.covariate.names)) & (!is.null(x$tp.covariate.names))){
para <- rbind((x$betap), t(x$sig2ep), t(x$sig2etap), t(x$sig2deltap), t(x$sig2op), t(x$phip), t(x$nup))
dimnames(para)[[1]] <- c(dimnames(x$X)[[2]], "sig2eps", "sig2eta", "sig2deltap", "sig2op", "phi", "nu")
}
else if((!is.null(x$sp.covariate.names)) & (!is.null(x$tp.covariate.names))){
para <- rbind((x$betap), t(x$sig2ep), t(x$sig2etap), t(x$sig2betap), t(x$sig2deltap), t(x$sig2op), t(x$phip), t(x$nup))
dimnames(para)[[1]] <- c(dimnames(x$X)[[2]], "sig2eps", "sig2eta", "sig2beta", "sig2deltap", "sig2op", "phi", "nu")
}
else {
stop("Error")
}
}
else {
if((is.null(x$sp.covariate.names)) & (is.null(x$tp.covariate.names))){
para <- rbind((x$betap), t(x$sig2ep), t(x$sig2etap), t(x$phip))
dimnames(para)[[1]] <- c(dimnames(x$X)[[2]], "sig2eps", "sig2eta", "phi")
}
else if((!is.null(x$sp.covariate.names)) & (is.null(x$tp.covariate.names))){
para <- rbind((x$betap), t(x$sig2ep), t(x$sig2etap), t(x$sig2betap), t(x$phip))
dimnames(para)[[1]] <- c(dimnames(x$X)[[2]], "sig2eps", "sig2eta", "sig2beta", "phi")
}
else if((is.null(x$sp.covariate.names)) & (!is.null(x$tp.covariate.names))){
para <- rbind((x$betap), t(x$sig2ep), t(x$sig2etap), t(x$sig2deltap), t(x$sig2op), t(x$phip))
dimnames(para)[[1]] <- c(dimnames(x$X)[[2]], "sig2eps", "sig2eta", "sig2deltap", "sig2op", "phi")
}
else if((!is.null(x$sp.covariate.names)) & (!is.null(x$tp.covariate.names))){
para <- rbind((x$betap), t(x$sig2ep), t(x$sig2etap), t(x$sig2betap), t(x$sig2deltap), t(x$sig2op), t(x$phip))
dimnames(para)[[1]] <- c(dimnames(x$X)[[2]], "sig2eps", "sig2eta", "sig2beta", "sig2deltap", "sig2op", "phi")
}
else {
stop("Error")
}
}
para<-t(para)
para<-mcmc(para)
para
}
else {
}
}
summary.spTD<-function(object, digits=4, package="spTDyn", coefficient=NULL, ...){
if(package=="coda"){
if(object$combined.fit.pred==TRUE){
stop("\n
}
else{
if(is.null(coefficient)){
cat("\n
if(!is.null(object$sp.covariate.names)) {cat("\n
tmp<-as.mcmc(object)
summary(tmp, ...)
}
else if(coefficient=="spatial"){
if(is.null(object$sp.covariate.names)) {stop("\n
cat("\n
tmp<-as.mcmc(t(object$betasp))
if(object$model=="GPP"){n<-object$knots}
else{n<-object$n}
tmp<-sp.dimname.fnc(x=tmp,names=object$sp.covariate.names,n=n,q=object$q)
summary(tmp, ...)
}
else if(coefficient=="temporal"){
if(is.null(object$tp.covariate.names)) {stop("\n
cat("\n
tmp<-as.mcmc(t(object$betatp))
tmp<-tp.dimname.fnc(x=tmp,names=object$tp.covariate.names,u=object$u,T=object$T)
summary(tmp, ...)
}
else if(coefficient=="rho"){
if(is.null(object$rhotp)) {stop("\n
cat("\n
tmp<-as.mcmc(t(object$rhotp))
dimnames(tmp)[[2]]<-paste("rho",1:object$u,sep="")
summary(tmp, ...)
}
else{
stop("Error: the argument coefficient only takes charecter 'spatial' and 'temporal'.")
}
}
}
else{
if(package=="spTDyn"){
coefficient <- coefficient
}
else if("Xsp"%in%names(object) | "Xtp"%in%names(object)){
coefficient <- coefficient
}
else{
coefficient <- NULL
}
if(is.null(coefficient)){
if((!is.null(object$sp.covariate.names)) & (!is.null(object$tp.covariate.names))){cat("\n
else if((!is.null(object$sp.covariate.names)) & (is.null(object$tp.covariate.names))) {cat("\n
else if((is.null(object$sp.covariate.names)) & (!is.null(object$tp.covariate.names))) {cat("\n
else { }
print(object)
cat("-----------------------------------------------------"); cat('\n');
cat("Parameters:\n")
print(round(object$parameter,digits=digits));
cat("-----------------------------------------------------"); cat('\n');
}
else if(coefficient=="spatial"){
if(is.null(object$sp.covariate.names)) {stop("\n
cat("\n
tmp<-as.mcmc(t(object$betasp))
if(object$model=="GPP"){n<-object$knots}
else{n<-object$n}
tmp<-sp.dimname.fnc(x=tmp,names=object$sp.covariate.names,n=n,q=object$q)
summary(tmp, ...)
}
else if(coefficient=="temporal"){
if(is.null(object$tp.covariate.names)) {stop("\n
cat("\n
tmp<-as.mcmc(t(object$betatp))
tmp<-tp.dimname.fnc(x=tmp,names=object$tp.covariate.names,u=object$u,T=object$T)
summary(tmp, ...)
}
else if(coefficient=="rho"){
if(is.null(object$rhotp)) {stop("\n
cat("\n
tmp<-as.mcmc(t(object$rhotp))
dimnames(tmp)[[2]]<-paste("rho",1:object$u,sep="")
summary(tmp, ...)
}
else{
stop("Error: the argument coefficient only takes character 'spatial' and 'temporal'.")
}
}
}
plot.spTD<-function(x, residuals=FALSE, coefficient=NULL, ...){
if(as.logical(residuals)==FALSE){
if(x$combined.fit.pred==TRUE){
if(!is.null(x$sp.covariate.names)) {cat("
cat("\n
plot(x$fitted[,1],residuals(x),ylab="Residuals",xlab="Fitted values");abline(h=0,lty=2);title("Residuals vs Fitted")
par(ask=TRUE)
qqnorm(residuals(x));qqline(residuals(x),lty=2)
}
else{
if(is.null(coefficient)){
if((!is.null(x$sp.covariate.names)) & (!is.null(x$tp.covariate.names))){cat("\n
else if((!is.null(x$sp.covariate.names)) & (is.null(x$tp.covariate.names))) {cat("\n
else if((is.null(x$sp.covariate.names)) & (!is.null(x$tp.covariate.names))) {cat("\n
else { }
if(x$model=="GP"){
tmp<-as.mcmc(x)
plot(tmp, ...)
}
else{
x$model="GP"
tmp<-as.mcmc(x)
plot(tmp, ...)
}
}
else if(coefficient=="spatial"){
if(is.null(x$sp.covariate.names)) {stop("\n
tmp<-as.mcmc(t(x$betasp))
if(x$model=="GPP"){n<-x$knots}
else{n<-x$n}
tmp<-sp.dimname.fnc(x=tmp,names=x$sp.covariate.names,n=n,q=x$q)
plot(tmp, ...)
}
else if(coefficient=="temporal"){
if(is.null(x$tp.covariate.names)) {stop("\n
tmp<-as.mcmc(t(x$betatp))
tmp<-tp.dimname.fnc(x=tmp,names=x$tp.covariate.names,u=x$u,T=x$T)
plot(tmp, ...)
}
else if(coefficient=="rho"){
if(is.null(x$rhotp)) {stop("\n
tmp<-as.mcmc(t(x$rhotp))
dimnames(tmp)[[2]]<-paste("rho",1:x$u,sep="")
plot(tmp, ...)
}
else{
stop("Error: the argument coefficient only takes charecter 'spatial' and 'temporal'.")
}
}
}
else{
if(!is.null(x$sp.covariate.names)) {cat("
plot(x$fitted[,1],residuals(x),ylab="Residuals",xlab="Fitted values");abline(h=0,lty=2);title("Residuals vs Fitted")
par(ask=TRUE)
qqnorm(residuals(x));qqline(residuals(x),lty=2)
}
}
sp.dimname.fnc<-function(x,names,n,q){
dimnames(x)[[2]][1:(n*q)]<-1:(n*q)
for(i in 1:q){
dimnames(x)[[2]][(1+(i-1)*n):(n*i)]<-rep(paste(names[i],"site",1:n,sep=""))
}
x
}
tp.dimname.fnc<-function(x,names,u,T){
dimnames(x)[[2]][1:(u*T)]<-1:(u*T)
for(i in 1:u){
dimnames(x)[[2]][(1+(i-1)*T):(T*i)]<-rep(paste(names[i],"time",1:T,sep=""))
}
x
}
spT.Summary.Stat <- function(y)
{
up.low.limit<-function(y,limit)
{
y<-sort(y)
y<-y[limit]
y
}
if(is.vector(y)==TRUE){
y <- matrix(y[!is.na(y)])
}
else{
y <- t(y)
}
N <- length(y[1, ])
nItr <- length(y[, 1])
z <- matrix(nrow = N, ncol = 5)
dimnames(z) <- list(dimnames(y)[[2]], c("Mean","Median","SD","Low2.5p","Up97.5p"))
if (nItr < 40) {
stop("\n
}
z[, 1] <- apply(y,2,mean)
z[, 2] <- apply(y,2,median)
z[, 3] <- apply(y,2,sd)
nl <- as.integer(nItr * 0.025)
nu <- as.integer(nItr * 0.975)
z[, 4] <- apply(y,2,up.low.limit,limit=nl)
z[, 5] <- apply(y,2,up.low.limit,limit=nu)
as.data.frame(z)
}
spT.segment.plot<-function(obs, est, up, low, limit=NULL){
tmp<-cbind(obs,est,up,low)
tmp<-na.omit(tmp)
if(is.null(limit)==TRUE){
plot(tmp[,1],tmp[,2],xlab="Observations",ylab="Predictions", pch="*",
xlim=c(min(c(tmp),na.rm=TRUE),max(c(tmp),na.rm=TRUE)),
ylim=c(min(c(tmp),na.rm=TRUE),max(c(tmp),na.rm=TRUE)))
}
else{
plot(tmp[,1],tmp[,2],xlab="Observations",ylab="Predictions",
xlim=c(limit[1],limit[2]),ylim=c(limit[1],limit[2]),pch="*")
}
segments(tmp[,1],tmp[,2],tmp[,1],tmp[,3])
segments(tmp[,1],tmp[,2],tmp[,1],tmp[,4])
}
spT.hit.false<-function(obs,fore,tol){
tmp<-cbind(obs,fore)
tmp<-na.omit(tmp)
c11<-tmp[tmp[,1]<=tol & tmp[,2]<=tol,]
c11<-length(c11)/2
c12<-tmp[tmp[,1]<=tol & tmp[,2]>tol,]
c12<-length(c12)/2
c21<-tmp[tmp[,1]>tol & tmp[,2]<=tol,]
c21<-length(c21)/2
c22<-tmp[tmp[,1]>tol & tmp[,2]>tol,]
c22<-length(c22)/2
mat<-matrix(c(c11,c21,c12,c22),2,2)
dimnames(mat)[[1]]<-c(paste("[Obs:<=",tol,"]"),paste("[Obs:> ",tol,"]"))
dimnames(mat)[[2]]<-c(paste("[Forecast:<=",tol,"]"),paste("[Forecast:>",tol,"]"))
POD<-round(mat[1,1]/sum(diag(mat)),4)
FAR<-round(mat[1,2]/sum(mat[1,]),4)
HAR<-round(sum(diag(mat))/sum(mat),4)
top<-2*(mat[1,1]*mat[2,2]-mat[1,2]*mat[2,1])
bot<-mat[1,2]^2+mat[2,1]^2+2*mat[1,1]*mat[2,2]+(mat[1,2]+mat[2,1])*sum(diag(mat))
S<-round(top/bot,4)
x<-list(False.Alarm=FAR,Hit.Rate=HAR,Probability.of.Detection=POD,
Heidke.Skill=S,cross.table=mat,tolerance.limit=tol)
x
}
spT.keep.morethan.dist <- function(coords, tol.dist=100)
{
a <- as.data.frame(coords)
names(a) <- c("long","lat")
n <- nrow(coords)
c1 <- rep(1:n, each=n)
c2 <- rep(1:n, n)
b <- matrix(NA, nrow=n, ncol=n)
w <- as.vector(upper.tri(b))
bigmat <- matrix(0, nrow=n*n, ncol=7)
bigmat[, 1] <- c1
bigmat[, 2] <- c2
bigmat[, 3] <- a$long[c1]
bigmat[, 4] <- a$lat[c1]
bigmat[, 5] <- a$long[c2]
bigmat[, 6] <- a$lat[c2]
ubmat <- bigmat[w, ]
ubmat[,7] <- as.vector(apply(ubmat[,3:6], 1, spT.geo_dist))
v <- ubmat[,7]
w <- ubmat[v<tol.dist, ]
z <- unique(w[,1])
a <- coords[-z, ]
a
}
PMCC<-function(z=NULL, z.mean=NULL, z.sd=NULL, z.samples=NULL)
{
if(is.null(z)){
stop("Error: need to provide z values.")
}
if(is.null(z.mean) | is.null(z.sd)){
if(!is.null(z.mean)){
stop("Error: need to provide z.sd value.")
}
if(!is.null(z.sd)){
stop("Error: need to provide z.mean value.")
}
if(is.null(z.samples)){
stop("Error: need to provide z.samples value.")
}
}
if(!is.null(z.samples)){
if ( !is.matrix(z.samples)) {
stop("Error: z.samples must be a (N x nItr) matrix")
}
if (dim(z.samples)[1] != length(z)) {
stop("Error: observations in z.samples in each iteration must be equal to length of z")
}
if ( dim(z.samples)[2] < 40) {
stop("Error: samples are too small to obtain summary statistics")
}
sum.stat = matrix(NA,length(c(z)),6)
sum.stat[,1:5] = as.matrix(spT.Summary.Stat(z.samples))
sum.stat[,6] = c(z)
sum.stat = sum.stat[!is.na(sum.stat[,6]),]
goodness.of.fit = round(sum((sum.stat[,1]-sum.stat[,6])^2),2)
penalty = round(sum(sum.stat[,3]^2),2)
pmcc = round(goodness.of.fit + penalty,2)
out = NULL
out$pmcc = pmcc;
out$goodness.of.fit = goodness.of.fit
out$penalty = penalty
out
}
else{
if(is.null(z.mean) | is.null(z.sd)){
stop("Error: need to provide z.mean and/or z.sd values.")
}
if(length(c(z)) != length(c(z.mean))){
stop("Error: z and z.mean should be in same length.")
}
if(length(c(z)) != length(c(z.sd))){
stop("Error: z and z.sd should be in same length.")
}
sum.stat = matrix(NA,length(c(z)),3)
sum.stat[,1] = c(z)
sum.stat[,2] = c(z.mean)
sum.stat[,3] = c(z.sd)
sum.stat = sum.stat[!is.na(sum.stat[,1]),]
goodness.of.fit = round(sum((sum.stat[,1]-sum.stat[,2])^2),2)
penalty = round(sum(sum.stat[,3]^2),2)
pmcc = round(goodness.of.fit + penalty,2)
out = NULL
out$pmcc = pmcc;
out$goodness.of.fit = goodness.of.fit
out$penalty = penalty
out
}
}
spT.check.locations<-function(fit.locations, pred.locations,
method="geodetic:km", tol=5){
if(!method %in% c("geodetic:km", "geodetic:mile", "euclidean",
"maximum", "manhattan", "canberra")){
stop("\n
}
coords.all <- rbind(fit.locations,pred.locations)
tn.fitsites <- length(fit.locations[, 1])
nfit.sites <- 1:tn.fitsites
tn.predsites <- length(coords.all[, 1]) - tn.fitsites
npred.sites <- (tn.fitsites + 1):(length(coords.all[, 1]))
if(method=="geodetic:km"){
coords.D <- as.matrix(spT.geodist(Lon=coords.all[,1],Lat=coords.all[,2], KM=TRUE))
}
else if(method=="geodetic:mile"){
coords.D <- as.matrix(spT.geodist(Lon=coords.all[,1],Lat=coords.all[,2], KM=FALSE))
}
else{
coords.D <- as.matrix(dist(coords.all, method, diag = T, upper = T))
}
coords.D[is.na(coords.D)]<-0
diag(coords.D)<-NA
fdmis<-coords.D[nfit.sites, npred.sites]
if(is.matrix(fdmis)==TRUE){
fdmis<-cbind(c(t(fdmis)),1:dim(fdmis)[[2]],sort(rep(1:dim(fdmis)[[1]],dim(fdmis)[[2]])))
fdmis<-fdmis[fdmis[,1] < tol,]
if(!is.na(fdmis[1])==TRUE){
cat("
cat("\n
fdmis<-matrix(fdmis,(length(fdmis)/3),3)
for(i in 1:dim(fdmis)[[1]]){
print(paste("Distance:", round(fdmis[i,1],2)," Predicted location:",fdmis[i,2]," Fitted location:", fdmis[i,3],""))
}
cat("
dimnames(fdmis)[[2]]<-c('distance','pred_location','fit_location')
fdmis
}
else{
cat("
cat("\n
}
}
else{
fdmis<-cbind(c(fdmis),1:length(fdmis))
fdmis<-fdmis[fdmis[,1] < tol,]
if(!is.na(fdmis[1])==TRUE){
cat("
cat("\n
fdmis<-matrix(fdmis)
for(i in 1:dim(fdmis)[[1]]){
print(paste("Distance:", round(fdmis[i,1],2)," Predicted location:",1," Fitted location:", fdmis[i,2],""))
}
cat("
dimnames(fdmis)[[2]]<-c('distance','fit_location')
fdmis
}
else{
cat("
cat("\n
}
}
}
spT.check.sites.inside<-function(coords, method, tol=0.1){
if(!method %in% c("geodetic:km", "geodetic:mile", "euclidean",
"maximum", "manhattan", "canberra")){
stop("\n
}
if(method=="geodetic:km"){
fdm<- as.matrix(spT.geodist(Lon=coords[,1],Lat=coords[,2], KM=TRUE))
}
else if(method=="geodetic:mile"){
fdm<- as.matrix(spT.geodist(Lon=coords[,1],Lat=coords[,2], KM=FALSE))
}
else{
fdm<- as.matrix(dist(coords, method, diag = TRUE, upper = TRUE))
}
diag(fdm)<-NA
fdm<-cbind(c(fdm),1:dim(fdm)[[2]],sort(rep(1:dim(fdm)[[1]],dim(fdm)[[2]])))
fdm<-fdm[!is.na(fdm[,1]),]
fdmis<-fdm[fdm[,1] < tol,]
if(!is.na(fdmis[1])==TRUE){
cat("
print(paste("( < ",tol," unit) to each other."))
fdmis<-matrix(fdmis,(length(fdmis)/3),3)
for(i in 1:dim(fdmis)[[1]]){
print(paste("Distance (unit):", round(fdmis[i,1],2)," site:",fdmis[i,2]," site:", fdmis[i,3],""))
}
dimnames(fdmis)[[2]]<-c('dist_km','pred_site','fit_site')
fdmis
cat("
stop("Error: Termination.")
}
}
fnc.time<-function(t)
{
if(t < 60){
t <- round(t,2)
tt <- paste(t," - Sec.")
cat(paste("
}
if(t < (60*60) && t >= 60){
t1 <- as.integer(t/60)
t <- round(t-t1*60,2)
tt <- paste(t1," - Mins.",t," - Sec.")
cat(paste("
}
if(t < (60*60*24) && t >= (60*60)){
t2 <- as.integer(t/(60*60))
t <- t-t2*60*60
t1 <- as.integer(t/60)
t <- round(t-t1*60,2)
tt <- paste(t2," - Hour/s.",t1," - Mins.",t," - Sec.")
cat(paste("
}
if(t >= (60*60*24)){
t3 <- as.integer(t/(60*60*24))
t <- t-t3*60*60*24
t2 <- as.integer(t/(60*60))
t <- t-t2*60*60
t1 <- as.integer(t/60)
t <- round(t-t1*60,2)
tt <- paste(t3," - Day/s.",t2," - Hour/s.",t1," - Mins.",t," - Sec.")
cat(paste("
}
tt
}
sp<-function(x){
class(x)<-"spBeta"
x
}
tp<-function(x){
class(x)<-"tpBeta"
x
}
ObsGridLoc<-function(obsLoc, gridLoc, distance.method="geodetic:km",
plot=FALSE)
{
if(!distance.method %in% c("geodetic:km", "geodetic:mile", "euclidean",
"maximum", "manhattan", "canberra")){
stop("\n
}
coords.all <- rbind(obsLoc, gridLoc)
n1.sites <- 1:dim(obsLoc)[[1]]
n2.sites <- (dim(obsLoc)[[1]] + 1):(dim(coords.all)[[1]])
if(distance.method=="geodetic:km"){
coords.D <- as.matrix(spT.geodist(Lon=coords.all[,1],Lat=coords.all[,2], KM=TRUE))
}
else if(distance.method=="geodetic:mile"){
coords.D <- as.matrix(spT.geodist(Lon=coords.all[,1],Lat=coords.all[,2], KM=FALSE))
}
else{
coords.D <- as.matrix(dist(coords.all, distance.method, diag = T, upper = T))
}
coords.D[is.na(coords.D)]<-0
diag(coords.D)<-NA
fdmis<-coords.D[n1.sites, n2.sites]
min.fnc<-function(x){
x<-cbind(x,1:length(x))
x<-x[order(x[,1]),]
x<-x[1,]
x
}
fdmis<-t(apply(fdmis,1,min.fnc))
fdmis<-cbind(obsLoc,gridLoc[fdmis[,2],],fdmis[,2],fdmis[,1])
dimnames(fdmis)[[2]]<-c("obsLon","obsLat","gridLon","gridLat","gridNum","dist")
if(plot==TRUE){
plot(fdmis[,1:2],pch="*")
points(fdmis[,3:4],pch=19,col=2)
legend("bottomleft",pch=c(8,19),col=c(1,2),legend=c("Observation locations", "Grid locations"),cex=0.9)
}
fdmis
}
gridTodata<-function(gridData, gridLoc=NULL, gridLon=NULL, gridLat=NULL)
{
options(warn=-1)
if(!is.null(gridLoc)){
lonlat<-gridLoc
dimnames(lonlat)[[2]]<-c("lon","lat")
}
else if((!is.null(gridLon)) & (!is.null(gridLat))){
lonlat<-expand.grid(gridLon, gridLat)
dimnames(lonlat)[[2]]<-c("lon","lat")
}
else{
stop("Error: check grid location input in the function")
}
if(is.list(gridData)){
n<-length(gridData)
if(n>1){
dat<-NULL
for(i in 1:n){
grid<-c(gridData[[i]])
dat<-cbind(dat,grid)
}
for(i in 1:n){ dimnames(dat)[[2]][i]<-paste("grid",i,sep="") }
dat<-cbind(lon=rep(lonlat[,1],length(c(gridData[[1]]))/dim(lonlat)[[1]]),
lat=rep(lonlat[,2],length(c(gridData[[1]]))/dim(lonlat)[[1]]),dat)
}
else{
dat<-cbind(lon=rep(lonlat[,1],length(c(gridData[[1]]))/dim(lonlat)[[1]]),
lat=rep(lonlat[,2],length(c(gridData[[1]]))/dim(lonlat)[[1]]),grid=c(gridData))
}
}
else{
dat<-cbind(lon=rep(lonlat[,1],length(c(gridData))/dim(lonlat)[[1]]),
lat=rep(lonlat[,2],length(c(gridData))/dim(lonlat)[[1]]),grid=c(gridData))
}
dat<-dat[order(dat[,1],dat[,2]),]
dat
}
ObsGridData<-function(obsData, gridData, obsLoc, gridLoc, distance.method="geodetic:km")
{
obsLoc<-as.matrix(obsLoc)
gridLoc<-as.matrix(gridLoc)
dimnames(obsLoc)<-NULL
dimnames(gridLoc)<-NULL
if((is.list(gridData)) & (is.list(gridLoc))){
n1<-length(gridData)
n2<-length(gridLoc)
if(n1 != n2){ stop("\n Error: list of gridData and gridLoc should be same") }
datt<-NULL
for(i in 1:n1){
gLoc<-gridLoc[[i]]
gLoc<-gLoc[order(gLoc[,1],gLoc[,2]),]
comb<-ObsGridLoc(obsLoc=obsLoc, gridLoc=gLoc, distance.method=distance.method)
grid<-gridTodata(gridData=gridData[[i]], gridLoc=gLoc)
grid<-cbind(gridNum=sort(rep(1:dim(gLoc)[[1]],dim(grid)[[1]]/dim(gLoc)[[1]])),grid)
dat<-NULL
for(j in comb[,5]){ dat<-rbind(dat,grid[grid[,1]==j,]) }
dimnames(dat)[[2]]<-c(paste("gridNum",i,sep=""),paste("grid.lon",i,sep=""),paste("grid.lat",i,sep=""),paste("grid",i,sep=""))
if(dim(obsData)[[1]] != dim(dat)[[1]]){ stop("Error: check the day and year for gridData\n obsData and gridData time points mismatch.\n")}
datt<-cbind(datt,dat)
}
dat<-cbind(obsData,datt); rm(datt);
dat
}
else{
comb<-ObsGridLoc(obsLoc=obsLoc, gridLoc=gridLoc[order(gridLoc[,1],gridLoc[,2]),], distance.method=distance.method)
grid<-gridTodata(gridData=gridData, gridLoc=gridLoc[order(gridLoc[,1],gridLoc[,2]),])
grid<-cbind(gridNum=sort(rep(1:dim(gridLoc)[[1]],dim(grid)[[1]]/dim(gridLoc)[[1]])),grid)
dat<-NULL
for(i in comb[,5]){ dat<-rbind(dat,grid[grid[,1]==i,]) }
dimnames(dat)[[2]][2:3]<-c("grid.lon","grid.lat")
if(dim(obsData)[[1]] != dim(dat)[[1]]){ stop("Error: check the day and year for gridData\n obsData and gridData time points mismatch.\n")}
dat<-cbind(obsData,dat)
dat
}
}
truncated.fnc<-function(Y, at=0, lambda=NULL, both=FALSE){
if(is.null(lambda)){
stop("Error: define truncation parameter lambda properly using list ")
}
if(is.null(at)){
stop("Error: define truncation point properly using list ")
}
if(at < 0){
stop("Error: currently truncation point only can take value >= zero ")
}
zm <- cbind(Y,Y-at,1)
zm[zm[, 2] <= 0, 3] <- 0
zm[, 2] <- zm[, 2]^(1/lambda)
zm[zm[, 3] == 0, 2] <- -(rgamma(nrow(zm[zm[,3]==0,]), shape=1, rate=1/(range(zm[zm[,3]==1,2],na.rm=TRUE)[[2]]/4.1)))
if (both == TRUE) {
zm
}
else {
c(zm[,2])
}
}
reverse.truncated.fnc<-function(Y, at=0, lambda=NULL){
if(is.null(lambda)){
stop("Error: define truncation parameter lambda properly using list ")
}
zm <- Y
zm[zm <= 0]<- 0
zm <- zm^(lambda)
zm <- zm + at
zm
}
prob.below.threshold <- function(out, at){
fnc<-function(x, at){
length(x[x <= at])/length(x)
}
apply(out,1,fnc,at=at)
} |
toMaxDiag_n <- function(pars, u, sigmaType, kKi, kLh, kLhi, kY, kX, kZ) {
kP <- ncol(kX)
kR <- length(kKi)
kK <- ncol(kZ)
kL <- sum(kLh)
beta <- pars[1:kP]
s0 <- length(pars[-(1:kP)])
ovSigma <- constructSigma(pars = pars[-(1:kP)], sigmaType = sigmaType, kK = kK, kR = kR, kLh = kLh, kLhi = kLhi)
if (min(eigen(ovSigma)$values) <= 0) {
return(list(value = -Inf, gradient = rep(0, length(pars)), hessian = matrix(0, length(pars), length(pars))))
}
return(qFunctionDiagCpp_n(beta, ovSigma, kKi, u, kY, kX, kZ))
} |
library(testthat)
library(tryCatchLog)
context("test_tryCatchLog_basics.R")
source("init_unit_test.R")
source("disable_logging_output.R")
test_that("log(-1) did throw a warning", {
expect_warning(log(-1))
})
test_that("log('abc') did throw an error", {
expect_error(log("abc"))
})
test_that("tryCatchLog creates no warning", {
expect_silent(tryCatchLog(log(1)))
})
test_that("tryCatchLog did throw a warning", {
expect_warning(tryCatchLog(log(-1)))
})
test_that("tryCatchLog did throw an error", {
expect_error(tryCatchLog(log("abc"), error = stop))
})
test_that("tryCatchLog did call the error handler", {
expect_equal(2, tryCatchLog({
flag <- 1
log("abc")
flag <- 3
},
error = function(e) {
flag <<- 2
}))
})
test_that("tryCatchLog with warning continues", {
withCallingHandlers(
tryCatchLog({
did.warn <- FALSE
flag <- 1
log(-1)
flag <- 2
})
,
warning = function(w) {
did.warn <<-
TRUE
invokeRestart("muffleWarning")
}
)
expect_equal(2, flag)
expect_true(did.warn)
})
test_that("tryCatchLog with message continues", {
withCallingHandlers(
tryCatchLog({
msg.sent <- FALSE
did.continue <- FALSE
throw.msg <- function()
message("read this message!")
throw.msg()
did.continue <- TRUE
})
,
message = function(w) {
msg.sent <<-
TRUE
invokeRestart("muffleMessage")
}
)
expect_true(did.continue)
expect_true(msg.sent)
})
test_that("tryCatchLog stops with an error and called the outer error function", {
tryCatch(
tryCatchLog({
did.raise.err <- FALSE
canceled <- TRUE
log("a")
canceled <- FALSE
},
error = stop),
error = function(e) {
did.raise.err <<- TRUE
}
)
expect_true(canceled)
expect_true(did.raise.err)
}) |
showCutPoints <- function(data) {
if (inherits(data, c("edsurvey.data.frame.list"))) {
return(itterateESDFL(match.call(), data))
}
checkDataClass(data, c("edsurvey.data.frame", "light.edsurvey.data.frame"))
cat(paste0("Achievement Levels:\n"))
als <- getAttributes(data, "achievementLevels")
if (length(als) > 0) {
for(i in 1:length(als)) {
cat(paste0(" ", names(als)[i], ": ", paste(unname(als[[i]]), collapse=", "), "\n"))
}
} else {
cat(paste0(" No achievement levels.\n"))
}
} |
SDMmodel2MaxEnt <- function(model) {
if (class(model@model) != "Maxent")
stop("'model' must be a SDMmodel object trained using the 'Maxent' method!")
maxent_model <- new("MaxEnt")
maxent_model@presence <- .get_presence(model@data)
maxent_model@absence <- .get_absence(model@data)
maxent_model@lambdas <- model@model@lambdas
maxent_model@hasabsence <- TRUE
return(maxent_model)
} |
test_that("orphanReactants function: output class is wrong", {
expect_true(is.vector(orphanReactants("2 A[c] + 3 B[c] => 5 D[c]")))
expect_true(is.vector(orphanReactants("2 5-A[c] + 3 B[c] <=> 5 3D[c]")))
})
test_that("orphanReactants function: output value is wrong", {
expect_equal(orphanReactants("2 A[c] + 3 B[c] => 5 D[c]"), c("A[c]", "B[c]"))
expect_equivalent(orphanReactants("2 5-A[c] + 3 B[c] <=> 5 3D[c]"),
c("3D[c]", "5-A[c]", "B[c]"))
}) |
fastML.init <- function(etas.obj){
params.ind =etas.obj$params.ind
params.fix =etas.obj$params.fix
nparams.etas =etas.obj$nparams.etas
nparams =etas.obj$nparams
ntheta =etas.obj$ntheta
params<-etas.obj$params
params.lim =c(0,0,0,0.9999,0,0,0)
fast.eps=etas.obj$fast.eps
params.etas =params[1:nparams.etas]
betacov =etas.obj$betacov
params.e =params.fix
params.e[params.ind==1]=exp(params.etas)+params.lim[params.ind==1]
lambda = params.e[1]
k0 = params.e[2]
c = params.e[3]
p = params.e[4]
gamma = params.e[5]
d = params.e[6]
q = params.e[7]
predictor =as.matrix(etas.obj$cov.matrix)%*%as.vector(betacov)+as.vector(etas.obj$offset)
eta=as.numeric(predictor)
x =etas.obj$cat$xcat.work
y =etas.obj$cat$ycat.work
t =etas.obj$cat$time.work
m =etas.obj$cat$magnitude
n <-length(x)
qq=list()
for(i in 2:n){
dx=x[i]-x[1:(i-1)]
dy=y[i]-y[1:(i-1)]
dt1=abs(t[i]-t[1:(i-1)])
ds=1/((dx*dx+dy*dy)/exp(gamma*m[i])+d)^q
dt=ds*exp(eta[i])*(dt1+c)^(-p)
qq[[i]]=max(dt)
print(c(i,qq[[i]]))
}
maxq=max(unlist(qq))
ind=array(0,n)
index.parz=numeric(0)
index.tot=numeric(0)
for(i in 2:n){
maxq=as.numeric(qq[[i]])
dx=x[i]-x[1:(i-1)]
dy=x[i]-x[1:(i-1)]
dt=abs(x[i]-x[1:(i-1)])
ds=1/((dx*dx+dy*dy)/exp(gamma*m[i])+d)^q
dt=ds*(dt+c)^(-p)*exp(eta[i])
index.parz=which(dt>fast.eps*maxq)
ind[i]=ind[i-1]+length(index.parz)
index.tot=c(index.tot,index.parz)
cat(c(i,length(index.parz)),";")
}
return(list(index.tot=index.tot,ind=ind))
} |
group_data <- function(.data) {
UseMethod("group_data")
}
group_data.data.frame <- function(.data) {
rows <- new_list_of(list(seq_len(nrow(.data))), ptype = integer())
new_data_frame(list(.rows = rows), n = 1L)
}
group_data.tbl_df <- function(.data) {
as_tibble(NextMethod())
}
group_data.rowwise_df <- function(.data) {
attr(.data, "groups")
}
group_data.grouped_df <- function(.data) {
attr(validate_grouped_df(.data), "groups")
}
group_keys <- function(.tbl, ...) {
UseMethod("group_keys")
}
group_keys.data.frame <- function(.tbl, ...) {
if (dots_n(...) > 0) {
lifecycle::deprecate_warn(
"1.0.0", "group_keys(... = )",
details = "Please `group_by()` first"
)
.tbl <- group_by(.tbl, ...)
}
out <- group_data(.tbl)
.Call(`dplyr_group_keys`, out)
}
group_rows <- function(.data) {
group_data(.data)[[".rows"]]
}
group_indices <- function(.data, ...) {
if (nargs() == 0) {
lifecycle::deprecate_warn("1.0.0", "group_indices()", "cur_group_id()")
return(cur_group_id())
}
UseMethod("group_indices")
}
group_indices.data.frame <- function(.data, ...) {
if (dots_n(...) > 0) {
lifecycle::deprecate_warn(
"1.0.0", "group_keys(... = )",
details = "Please `group_by()` first"
)
.data <- group_by(.data, ...)
}
.Call(`dplyr_group_indices`, .data, group_rows(.data))
}
group_vars <- function(x) {
UseMethod("group_vars")
}
group_vars.data.frame <- function(x) {
setdiff(names(group_data(x)), ".rows")
}
groups <- function(x) {
UseMethod("groups")
}
groups.data.frame <- function(x) {
syms(group_vars(x))
}
group_size <- function(x) UseMethod("group_size")
group_size.data.frame <- function(x) {
lengths(group_rows(x))
}
n_groups <- function(x) UseMethod("n_groups")
n_groups.data.frame <- function(x) {
nrow(group_data(x))
} |
PredStempCens = function(Est.StempCens, locPre, timePre, xPre){
if(class(Est.StempCens)!="Est.StempCens") stop("An object of the class Est.StempCens must be provided")
if(class(locPre)!="matrix" & class(locPre)!="data.frame") stop("locPre must be a matrix or data.frame")
if(class(xPre)!="matrix") stop("xPre must be a matrix")
if(class(timePre)!="matrix" & class(timePre)!="data.frame") stop("timePre must be a matrix or data.frame")
if(nrow(locPre)!=nrow(timePre)) stop("The number of rows in locPre must be equal to the length of timePre")
if(nrow(locPre)!=nrow(xPre)) stop("The number of rows in locPre must be equal to the number of rows in xPre")
model = Est.StempCens
loc.Pre = locPre
time.Pre = timePre
x.pre = xPre
ypred <- PredictNewValues(model,loc.Pre,time.Pre,x.pre)
out.ST <- ypred
class(out.ST) <- "Pred.StempCens"
return(invisible(out.ST))
} |
`%like%` <- function(var, pattern){
stringi::stri_detect_regex(var, pattern, case_insensitive = TRUE)
}
`%LIKE%` <- `%like%`
`%slike%` <- function(var, pattern){
stringi::stri_detect_regex(var, pattern, case_insensitive = FALSE)
}
`%SLIKE%` <- `%slike%` |
setMethod("posterior_predict", "ubmsFit",
function(object, param=c("y","z"), draws=NULL, re.form=NULL, ...){
param <- match.arg(param, c("y", "z"))
nsamp <- nsamples(object)
samp_inds <- get_samples(object, draws)
switch(param,
"z" = sim_z(object, samples=samp_inds, re.form=re.form),
"y" = sim_y(object, samples=samp_inds, re.form=re.form))
})
setGeneric("sim_z", function(object, ...) standardGeneric("sim_z"))
setGeneric("sim_y", function(object, ...) standardGeneric("sim_y"))
process_z <- function(object, samples, re.form, z){
if(is.null(z)){
z <- t(sim_z(object, samples=samples, re.form=re.form))
} else {
z <- t(z)
}
z
} |
rulefile <- tempfile(fileext=".R")
writeLines("rules:\n-\n expr:\n name:", con=rulefile)
expect_warning(validator(.file = rulefile)) |
context("test-ahull.R")
test_that("Triangles ", {
ex1 <- ahull(, c(0, 1, 2, 3, 4), c(0, 1, 2, 0, 0))
expect_equal(ex1$area, 1.5)
expect_equal(ex1$xmin, 1)
ex2 <- ahull(data.table(x = c(1:6),
y = c(0, 2, 1.5, 2, 0.9, -1)))
ex3 <- ahull(data.table(x = c(1:6),
y = -c(0, 2, 1.5, 2, 0.9, -1)),
incl_negative = TRUE)
ex4 <- ahull(data.table(x = c(1:6),
y = c(0, 2, 1.5, 2, 0.9, -1)),
maximize = "")
expect_equal(ex2$ymax, 1.5)
expect_equal(ex3$ymin, -1.5)
ex5 <- ahull(data.table(x = 1:5,
y = c(0, 1, 0, 1, 0)))
expect_equal(ex5[["h"]], 0)
expect_true(is.na(area_from_min(1L, data.table(x = 1:6, y = c(0, 1, 0, 1, 0, 0)))[["xmax"]]))
expect_equal(area_from_min(1L, data.table(x = 1:6, y = c(0, 1, 0, 1, 0, 1)))[["h"]], 0)
expect_equal(area_from_min(6L, data.table(x = 1:6, y = c(0, 1, 0, 1, 0, 0)))[["h"]], 0)
expect_equal(area_from_min(2L, data.table(x = 1:6, y = c(0, 1, 0, 1, 0, 0)))[["h"]], 1)
expect_true(is.na(area_from_min(2L, data.table(x = 1:6, y = c(0, 1, 0, 1, 0, 0)))[["w"]]))
expect_true(is.na(area_from_min(4L, data.table(x = 1:6, y = c(0.1, 1, 0, 1, 0.2, 0)))[["w"]]))
})
test_that("warnings", {
expect_warning(ahull(, 0, 0))
})
test_that("mini-utils", {
library(magrittr)
library(data.table)
expect_identical(A(1, 1, 2, 2, 3, 0),
list(0.5, 2.5))
expect_identical(height2x(1.5, 1:5, c(1, 2, -1, 1, 2)),
c(2+1/6, 1.5, 4.5))
expect_error(height2x(1.5, 5:1, c(1, 2, -1, 1, 2)),
regexp = "sorted")
dtemi <- data.table(x = 1:5,
y = c(0.03, 0.49, 0, 0.65, 1))
expect_identical(areas_right_of(dtemi),
c(1L, 3L))
dtemil_1nna <-
areas_right_of(dtemi, return_ind = FALSE)[[1L]] %>%
as.double %>%
.[!is.na(.)]
expect_equal(dtemil_1nna, 0.23, tol = 0.001)
})
test_that("Corners", {
h0 <- ahull(, c(0:4), rep(-1, 5))
expect_equal(h0[["h"]], 0)
hxy <- ahull(, 0:4, c(0, 1, 2, 1, 0.5))
expect_equal(hxy[["area"]], 1.75)
h01 <- ahull(, c(0:4), c(0, 1, 2, -1, 4))
expect_equal(h01[["xmax"]], 2+1/3)
})
test_that("Hulls may be above 0 even if the next point on the curve is negative", {
skip("Not yet considered")
}) |
plot.cdslist <- function(x, which = 2L, ...){
loss <- sapply(x, "[[", "minloss")
K <- sapply(x, "[[", "K")
plot(K, loss, type = "b", pch = 16, main = "Scree Plot", xlab = "K", ylab = "Loss")
invisible(lapply(x, plot, which = which, ...))
} |
plot_number_of_repetitions <- function(x, ...) {
mapping <- attr(x, "mapping")
level <- attr(x, "level")
units <- attr(x, "units")
type <- attr(x,"type")
absolute <- NULL
if(level == "log") {
attr(x, "raw") %>%
ggplot(aes("", absolute)) +
geom_boxplot() +
scale_y_continuous() +
theme_light() +
coord_flip() +
labs(x = "", y = glue("Number of {type} repetitions (per case)")) -> p
}
else if(level == "case") {
x %>%
ggplot(aes_string(glue("reorder({mapping$case_id}, absolute)"), "absolute")) +
geom_col(aes(fill = absolute)) +
scale_fill_continuous_tableau(palette = "Blue", name = "Number of repetitions") +
labs(x = "Cases", y = glue("Number of {type} repetitions")) +
scale_y_continuous() +
coord_flip() +
theme_light() +
theme(axis.text.x = element_blank()) +
scale_x_discrete(breaks = NULL) -> p
}
else if(level == "activity") {
x %>%
ggplot(aes_string(glue("reorder({mapping$activity_id}, absolute)"), "absolute")) +
geom_col(aes(fill = absolute)) +
scale_y_continuous() +
theme_light() +
coord_flip() +
labs(x = "Activity", y = glue("Number of {type} repetitions")) -> p
}
else if(level == "resource") {
if(type %in% c("redo", "all")) {
x %>%
ggplot(aes_string(glue("reorder(first_resource, absolute)"), "absolute")) +
geom_col(aes(fill = absolute)) +
scale_y_continuous() +
theme_light() +
coord_flip() +
labs(x = "First resource", y = glue("Number of {type} repetitions")) -> p
}
else if (type == "repeat") {
x %>%
ggplot(aes_string(glue("reorder({mapping$resource_id}, absolute)"), "absolute")) +
geom_col(aes(fill = absolute)) +
scale_y_continuous() +
theme_light() +
coord_flip() +
labs(x = "Resource", y = glue("Number of {type} repetitions")) -> p
}
}
else if(level == "resource-activity") {
if(type %in% c("redo", "all")) {
x %>%
ggplot(aes_string(mapping$activity_id, "first_resource")) +
geom_tile(aes(fill = absolute)) +
geom_text(aes(label = absolute), fontface = "bold", color = "white") +
theme_light() +
theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
scale_fill_continuous_tableau(glue("Number of {type} repetitions"), palette = "Blue") +
labs(x = "Activity", y = "First resource") -> p
}
else if (type == "repeat") {
x %>%
ggplot(aes_string(mapping$activity_id, mapping$resource_id)) +
geom_tile(aes(fill = absolute)) +
geom_text(aes(label = absolute), fontface = "bold", color = "white") +
theme_light() +
theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
scale_fill_continuous_tableau(glue("Number of {type} repetitions"), palette = "Blue") +
labs(x = "Activity", y = "Resource") -> p
}
}
if(!is.null(attr(x, "groups"))) {
p <- p + facet_grid(as.formula(paste(c(paste(attr(x, "groups"), collapse = "+"), "~." ), collapse = "")), scales = "free_y")
}
return(p)
} |
wbt_change_vector_analysis <- function(date1, date2, magnitude, direction, wd=NULL, verbose_mode=FALSE, compress_rasters=FALSE) {
wbt_init()
args <- ""
args <- paste(args, paste0("--date1=", date1))
args <- paste(args, paste0("--date2=", date2))
args <- paste(args, paste0("--magnitude=", magnitude))
args <- paste(args, paste0("--direction=", direction))
if (!is.null(wd)) {
args <- paste(args, paste0("--wd=", wd))
}
if (compress_rasters) {
args <- paste(args, "--compress_rasters")
}
tool_name <- "change_vector_analysis"
wbt_run_tool(tool_name, args, verbose_mode)
}
wbt_closing <- function(input, output, filterx=11, filtery=11, wd=NULL, verbose_mode=FALSE, compress_rasters=FALSE) {
wbt_init()
args <- ""
args <- paste(args, paste0("--input=", input))
args <- paste(args, paste0("--output=", output))
if (!is.null(filterx)) {
args <- paste(args, paste0("--filterx=", filterx))
}
if (!is.null(filtery)) {
args <- paste(args, paste0("--filtery=", filtery))
}
if (!is.null(wd)) {
args <- paste(args, paste0("--wd=", wd))
}
if (compress_rasters) {
args <- paste(args, "--compress_rasters")
}
tool_name <- "closing"
wbt_run_tool(tool_name, args, verbose_mode)
}
wbt_create_colour_composite <- function(red, green, blue, output, opacity=NULL, enhance=TRUE, zeros=FALSE, wd=NULL, verbose_mode=FALSE, compress_rasters=FALSE) {
wbt_init()
args <- ""
args <- paste(args, paste0("--red=", red))
args <- paste(args, paste0("--green=", green))
args <- paste(args, paste0("--blue=", blue))
args <- paste(args, paste0("--output=", output))
if (!is.null(opacity)) {
args <- paste(args, paste0("--opacity=", opacity))
}
if (enhance) {
args <- paste(args, "--enhance")
}
if (zeros) {
args <- paste(args, "--zeros")
}
if (!is.null(wd)) {
args <- paste(args, paste0("--wd=", wd))
}
if (compress_rasters) {
args <- paste(args, "--compress_rasters")
}
tool_name <- "create_colour_composite"
wbt_run_tool(tool_name, args, verbose_mode)
}
wbt_flip_image <- function(input, output, direction="vertical", wd=NULL, verbose_mode=FALSE, compress_rasters=FALSE) {
wbt_init()
args <- ""
args <- paste(args, paste0("--input=", input))
args <- paste(args, paste0("--output=", output))
if (!is.null(direction)) {
args <- paste(args, paste0("--direction=", direction))
}
if (!is.null(wd)) {
args <- paste(args, paste0("--wd=", wd))
}
if (compress_rasters) {
args <- paste(args, "--compress_rasters")
}
tool_name <- "flip_image"
wbt_run_tool(tool_name, args, verbose_mode)
}
wbt_ihs_to_rgb <- function(intensity, hue, saturation, red=NULL, green=NULL, blue=NULL, output=NULL, wd=NULL, verbose_mode=FALSE, compress_rasters=FALSE) {
wbt_init()
args <- ""
args <- paste(args, paste0("--intensity=", intensity))
args <- paste(args, paste0("--hue=", hue))
args <- paste(args, paste0("--saturation=", saturation))
if (!is.null(red)) {
args <- paste(args, paste0("--red=", red))
}
if (!is.null(green)) {
args <- paste(args, paste0("--green=", green))
}
if (!is.null(blue)) {
args <- paste(args, paste0("--blue=", blue))
}
if (!is.null(output)) {
args <- paste(args, paste0("--output=", output))
}
if (!is.null(wd)) {
args <- paste(args, paste0("--wd=", wd))
}
if (compress_rasters) {
args <- paste(args, "--compress_rasters")
}
tool_name <- "ihs_to_rgb"
wbt_run_tool(tool_name, args, verbose_mode)
}
wbt_image_slider <- function(input1, input2, output, palette1="grey", reverse1=FALSE, label1="", palette2="grey", reverse2=FALSE, label2="", height=600, wd=NULL, verbose_mode=FALSE, compress_rasters=FALSE) {
wbt_init()
args <- ""
args <- paste(args, paste0("--input1=", input1))
args <- paste(args, paste0("--input2=", input2))
args <- paste(args, paste0("--output=", output))
if (!is.null(palette1)) {
args <- paste(args, paste0("--palette1=", palette1))
}
if (reverse1) {
args <- paste(args, "--reverse1")
}
if (!is.null(label1)) {
args <- paste(args, paste0("--label1=", label1))
}
if (!is.null(palette2)) {
args <- paste(args, paste0("--palette2=", palette2))
}
if (reverse2) {
args <- paste(args, "--reverse2")
}
if (!is.null(label2)) {
args <- paste(args, paste0("--label2=", label2))
}
if (!is.null(height)) {
args <- paste(args, paste0("--height=", height))
}
if (!is.null(wd)) {
args <- paste(args, paste0("--wd=", wd))
}
if (compress_rasters) {
args <- paste(args, "--compress_rasters")
}
tool_name <- "image_slider"
wbt_run_tool(tool_name, args, verbose_mode)
}
wbt_image_stack_profile <- function(inputs, points, output, wd=NULL, verbose_mode=FALSE, compress_rasters=FALSE) {
wbt_init()
args <- ""
args <- paste(args, paste0("--inputs=", inputs))
args <- paste(args, paste0("--points=", points))
args <- paste(args, paste0("--output=", output))
if (!is.null(wd)) {
args <- paste(args, paste0("--wd=", wd))
}
if (compress_rasters) {
args <- paste(args, "--compress_rasters")
}
tool_name <- "image_stack_profile"
wbt_run_tool(tool_name, args, verbose_mode)
}
wbt_integral_image <- function(input, output, wd=NULL, verbose_mode=FALSE, compress_rasters=FALSE) {
wbt_init()
args <- ""
args <- paste(args, paste0("--input=", input))
args <- paste(args, paste0("--output=", output))
if (!is.null(wd)) {
args <- paste(args, paste0("--wd=", wd))
}
if (compress_rasters) {
args <- paste(args, "--compress_rasters")
}
tool_name <- "integral_image"
wbt_run_tool(tool_name, args, verbose_mode)
}
wbt_line_thinning <- function(input, output, wd=NULL, verbose_mode=FALSE, compress_rasters=FALSE) {
wbt_init()
args <- ""
args <- paste(args, paste0("--input=", input))
args <- paste(args, paste0("--output=", output))
if (!is.null(wd)) {
args <- paste(args, paste0("--wd=", wd))
}
if (compress_rasters) {
args <- paste(args, "--compress_rasters")
}
tool_name <- "line_thinning"
wbt_run_tool(tool_name, args, verbose_mode)
}
wbt_mosaic <- function(output, inputs=NULL, method="nn", wd=NULL, verbose_mode=FALSE, compress_rasters=FALSE) {
wbt_init()
args <- ""
args <- paste(args, paste0("--output=", output))
if (!is.null(inputs)) {
args <- paste(args, paste0("--inputs=", inputs))
}
if (!is.null(method)) {
args <- paste(args, paste0("--method=", method))
}
if (!is.null(wd)) {
args <- paste(args, paste0("--wd=", wd))
}
if (compress_rasters) {
args <- paste(args, "--compress_rasters")
}
tool_name <- "mosaic"
wbt_run_tool(tool_name, args, verbose_mode)
}
wbt_mosaic_with_feathering <- function(input1, input2, output, method="cc", weight=4.0, wd=NULL, verbose_mode=FALSE, compress_rasters=FALSE) {
wbt_init()
args <- ""
args <- paste(args, paste0("--input1=", input1))
args <- paste(args, paste0("--input2=", input2))
args <- paste(args, paste0("--output=", output))
if (!is.null(method)) {
args <- paste(args, paste0("--method=", method))
}
if (!is.null(weight)) {
args <- paste(args, paste0("--weight=", weight))
}
if (!is.null(wd)) {
args <- paste(args, paste0("--wd=", wd))
}
if (compress_rasters) {
args <- paste(args, "--compress_rasters")
}
tool_name <- "mosaic_with_feathering"
wbt_run_tool(tool_name, args, verbose_mode)
}
wbt_normalized_difference_index <- function(input1, input2, output, clip=0.0, correction=0.0, wd=NULL, verbose_mode=FALSE, compress_rasters=FALSE) {
wbt_init()
args <- ""
args <- paste(args, paste0("--input1=", input1))
args <- paste(args, paste0("--input2=", input2))
args <- paste(args, paste0("--output=", output))
if (!is.null(clip)) {
args <- paste(args, paste0("--clip=", clip))
}
if (!is.null(correction)) {
args <- paste(args, paste0("--correction=", correction))
}
if (!is.null(wd)) {
args <- paste(args, paste0("--wd=", wd))
}
if (compress_rasters) {
args <- paste(args, "--compress_rasters")
}
tool_name <- "normalized_difference_index"
wbt_run_tool(tool_name, args, verbose_mode)
}
wbt_opening <- function(input, output, filterx=11, filtery=11, wd=NULL, verbose_mode=FALSE, compress_rasters=FALSE) {
wbt_init()
args <- ""
args <- paste(args, paste0("--input=", input))
args <- paste(args, paste0("--output=", output))
if (!is.null(filterx)) {
args <- paste(args, paste0("--filterx=", filterx))
}
if (!is.null(filtery)) {
args <- paste(args, paste0("--filtery=", filtery))
}
if (!is.null(wd)) {
args <- paste(args, paste0("--wd=", wd))
}
if (compress_rasters) {
args <- paste(args, "--compress_rasters")
}
tool_name <- "opening"
wbt_run_tool(tool_name, args, verbose_mode)
}
wbt_remove_spurs <- function(input, output, iterations=10, wd=NULL, verbose_mode=FALSE, compress_rasters=FALSE) {
wbt_init()
args <- ""
args <- paste(args, paste0("--input=", input))
args <- paste(args, paste0("--output=", output))
if (!is.null(iterations)) {
args <- paste(args, paste0("--iterations=", iterations))
}
if (!is.null(wd)) {
args <- paste(args, paste0("--wd=", wd))
}
if (compress_rasters) {
args <- paste(args, "--compress_rasters")
}
tool_name <- "remove_spurs"
wbt_run_tool(tool_name, args, verbose_mode)
}
wbt_resample <- function(inputs, output, cell_size=NULL, base=NULL, method="cc", wd=NULL, verbose_mode=FALSE, compress_rasters=FALSE) {
wbt_init()
args <- ""
args <- paste(args, paste0("--inputs=", inputs))
args <- paste(args, paste0("--output=", output))
if (!is.null(cell_size)) {
args <- paste(args, paste0("--cell_size=", cell_size))
}
if (!is.null(base)) {
args <- paste(args, paste0("--base=", base))
}
if (!is.null(method)) {
args <- paste(args, paste0("--method=", method))
}
if (!is.null(wd)) {
args <- paste(args, paste0("--wd=", wd))
}
if (compress_rasters) {
args <- paste(args, "--compress_rasters")
}
tool_name <- "resample"
wbt_run_tool(tool_name, args, verbose_mode)
}
wbt_rgb_to_ihs <- function(intensity, hue, saturation, red=NULL, green=NULL, blue=NULL, composite=NULL, wd=NULL, verbose_mode=FALSE, compress_rasters=FALSE) {
wbt_init()
args <- ""
args <- paste(args, paste0("--intensity=", intensity))
args <- paste(args, paste0("--hue=", hue))
args <- paste(args, paste0("--saturation=", saturation))
if (!is.null(red)) {
args <- paste(args, paste0("--red=", red))
}
if (!is.null(green)) {
args <- paste(args, paste0("--green=", green))
}
if (!is.null(blue)) {
args <- paste(args, paste0("--blue=", blue))
}
if (!is.null(composite)) {
args <- paste(args, paste0("--composite=", composite))
}
if (!is.null(wd)) {
args <- paste(args, paste0("--wd=", wd))
}
if (compress_rasters) {
args <- paste(args, "--compress_rasters")
}
tool_name <- "rgb_to_ihs"
wbt_run_tool(tool_name, args, verbose_mode)
}
wbt_split_colour_composite <- function(input, red=NULL, green=NULL, blue=NULL, wd=NULL, verbose_mode=FALSE, compress_rasters=FALSE) {
wbt_init()
args <- ""
args <- paste(args, paste0("--input=", input))
if (!is.null(red)) {
args <- paste(args, paste0("--red=", red))
}
if (!is.null(green)) {
args <- paste(args, paste0("--green=", green))
}
if (!is.null(blue)) {
args <- paste(args, paste0("--blue=", blue))
}
if (!is.null(wd)) {
args <- paste(args, paste0("--wd=", wd))
}
if (compress_rasters) {
args <- paste(args, "--compress_rasters")
}
tool_name <- "split_colour_composite"
wbt_run_tool(tool_name, args, verbose_mode)
}
wbt_thicken_raster_line <- function(input, output, wd=NULL, verbose_mode=FALSE, compress_rasters=FALSE) {
wbt_init()
args <- ""
args <- paste(args, paste0("--input=", input))
args <- paste(args, paste0("--output=", output))
if (!is.null(wd)) {
args <- paste(args, paste0("--wd=", wd))
}
if (compress_rasters) {
args <- paste(args, "--compress_rasters")
}
tool_name <- "thicken_raster_line"
wbt_run_tool(tool_name, args, verbose_mode)
}
wbt_tophat_transform <- function(input, output, filterx=11, filtery=11, variant="white", wd=NULL, verbose_mode=FALSE, compress_rasters=FALSE) {
wbt_init()
args <- ""
args <- paste(args, paste0("--input=", input))
args <- paste(args, paste0("--output=", output))
if (!is.null(filterx)) {
args <- paste(args, paste0("--filterx=", filterx))
}
if (!is.null(filtery)) {
args <- paste(args, paste0("--filtery=", filtery))
}
if (!is.null(variant)) {
args <- paste(args, paste0("--variant=", variant))
}
if (!is.null(wd)) {
args <- paste(args, paste0("--wd=", wd))
}
if (compress_rasters) {
args <- paste(args, "--compress_rasters")
}
tool_name <- "tophat_transform"
wbt_run_tool(tool_name, args, verbose_mode)
}
wbt_write_function_memory_insertion <- function(input1, input2, output, input3=NULL, wd=NULL, verbose_mode=FALSE, compress_rasters=FALSE) {
wbt_init()
args <- ""
args <- paste(args, paste0("--input1=", input1))
args <- paste(args, paste0("--input2=", input2))
args <- paste(args, paste0("--output=", output))
if (!is.null(input3)) {
args <- paste(args, paste0("--input3=", input3))
}
if (!is.null(wd)) {
args <- paste(args, paste0("--wd=", wd))
}
if (compress_rasters) {
args <- paste(args, "--compress_rasters")
}
tool_name <- "write_function_memory_insertion"
wbt_run_tool(tool_name, args, verbose_mode)
}
wbt_evaluate_training_sites <- function(inputs, polys, field, output, wd=NULL, verbose_mode=FALSE, compress_rasters=FALSE) {
wbt_init()
args <- ""
args <- paste(args, paste0("--inputs=", inputs))
args <- paste(args, paste0("--polys=", polys))
args <- paste(args, paste0("--field=", field))
args <- paste(args, paste0("--output=", output))
if (!is.null(wd)) {
args <- paste(args, paste0("--wd=", wd))
}
if (compress_rasters) {
args <- paste(args, "--compress_rasters")
}
tool_name <- "evaluate_training_sites"
wbt_run_tool(tool_name, args, verbose_mode)
}
wbt_generalize_classified_raster <- function(input, output, min_size=4, method="longest", wd=NULL, verbose_mode=FALSE, compress_rasters=FALSE) {
wbt_init()
args <- ""
args <- paste(args, paste0("--input=", input))
args <- paste(args, paste0("--output=", output))
if (!is.null(min_size)) {
args <- paste(args, paste0("--min_size=", min_size))
}
if (!is.null(method)) {
args <- paste(args, paste0("--method=", method))
}
if (!is.null(wd)) {
args <- paste(args, paste0("--wd=", wd))
}
if (compress_rasters) {
args <- paste(args, "--compress_rasters")
}
tool_name <- "generalize_classified_raster"
wbt_run_tool(tool_name, args, verbose_mode)
}
wbt_generalize_with_similarity <- function(input, similarity, output, min_size=4, wd=NULL, verbose_mode=FALSE, compress_rasters=FALSE) {
wbt_init()
args <- ""
args <- paste(args, paste0("--input=", input))
args <- paste(args, paste0("--similarity=", similarity))
args <- paste(args, paste0("--output=", output))
if (!is.null(min_size)) {
args <- paste(args, paste0("--min_size=", min_size))
}
if (!is.null(wd)) {
args <- paste(args, paste0("--wd=", wd))
}
if (compress_rasters) {
args <- paste(args, "--compress_rasters")
}
tool_name <- "generalize_with_similarity"
wbt_run_tool(tool_name, args, verbose_mode)
}
wbt_image_segmentation <- function(inputs, output, threshold=0.5, steps=10, min_area=4, wd=NULL, verbose_mode=FALSE, compress_rasters=FALSE) {
wbt_init()
args <- ""
args <- paste(args, paste0("--inputs=", inputs))
args <- paste(args, paste0("--output=", output))
if (!is.null(threshold)) {
args <- paste(args, paste0("--threshold=", threshold))
}
if (!is.null(steps)) {
args <- paste(args, paste0("--steps=", steps))
}
if (!is.null(min_area)) {
args <- paste(args, paste0("--min_area=", min_area))
}
if (!is.null(wd)) {
args <- paste(args, paste0("--wd=", wd))
}
if (compress_rasters) {
args <- paste(args, "--compress_rasters")
}
tool_name <- "image_segmentation"
wbt_run_tool(tool_name, args, verbose_mode)
}
wbt_min_dist_classification <- function(inputs, polys, field, output, threshold=NULL, wd=NULL, verbose_mode=FALSE, compress_rasters=FALSE) {
wbt_init()
args <- ""
args <- paste(args, paste0("--inputs=", inputs))
args <- paste(args, paste0("--polys=", polys))
args <- paste(args, paste0("--field=", field))
args <- paste(args, paste0("--output=", output))
if (!is.null(threshold)) {
args <- paste(args, paste0("--threshold=", threshold))
}
if (!is.null(wd)) {
args <- paste(args, paste0("--wd=", wd))
}
if (compress_rasters) {
args <- paste(args, "--compress_rasters")
}
tool_name <- "min_dist_classification"
wbt_run_tool(tool_name, args, verbose_mode)
}
wbt_parallelepiped_classification <- function(inputs, polys, field, output, wd=NULL, verbose_mode=FALSE, compress_rasters=FALSE) {
wbt_init()
args <- ""
args <- paste(args, paste0("--inputs=", inputs))
args <- paste(args, paste0("--polys=", polys))
args <- paste(args, paste0("--field=", field))
args <- paste(args, paste0("--output=", output))
if (!is.null(wd)) {
args <- paste(args, paste0("--wd=", wd))
}
if (compress_rasters) {
args <- paste(args, "--compress_rasters")
}
tool_name <- "parallelepiped_classification"
wbt_run_tool(tool_name, args, verbose_mode)
}
wbt_adaptive_filter <- function(input, output, filterx=11, filtery=11, threshold=2.0, wd=NULL, verbose_mode=FALSE, compress_rasters=FALSE) {
wbt_init()
args <- ""
args <- paste(args, paste0("--input=", input))
args <- paste(args, paste0("--output=", output))
if (!is.null(filterx)) {
args <- paste(args, paste0("--filterx=", filterx))
}
if (!is.null(filtery)) {
args <- paste(args, paste0("--filtery=", filtery))
}
if (!is.null(threshold)) {
args <- paste(args, paste0("--threshold=", threshold))
}
if (!is.null(wd)) {
args <- paste(args, paste0("--wd=", wd))
}
if (compress_rasters) {
args <- paste(args, "--compress_rasters")
}
tool_name <- "adaptive_filter"
wbt_run_tool(tool_name, args, verbose_mode)
}
wbt_bilateral_filter <- function(input, output, sigma_dist=0.75, sigma_int=1.0, wd=NULL, verbose_mode=FALSE, compress_rasters=FALSE) {
wbt_init()
args <- ""
args <- paste(args, paste0("--input=", input))
args <- paste(args, paste0("--output=", output))
if (!is.null(sigma_dist)) {
args <- paste(args, paste0("--sigma_dist=", sigma_dist))
}
if (!is.null(sigma_int)) {
args <- paste(args, paste0("--sigma_int=", sigma_int))
}
if (!is.null(wd)) {
args <- paste(args, paste0("--wd=", wd))
}
if (compress_rasters) {
args <- paste(args, "--compress_rasters")
}
tool_name <- "bilateral_filter"
wbt_run_tool(tool_name, args, verbose_mode)
}
wbt_canny_edge_detection <- function(input, output, sigma=0.5, low=0.05, high=0.15, add_back=FALSE, wd=NULL, verbose_mode=FALSE, compress_rasters=FALSE) {
wbt_init()
args <- ""
args <- paste(args, paste0("--input=", input))
args <- paste(args, paste0("--output=", output))
if (!is.null(sigma)) {
args <- paste(args, paste0("--sigma=", sigma))
}
if (!is.null(low)) {
args <- paste(args, paste0("--low=", low))
}
if (!is.null(high)) {
args <- paste(args, paste0("--high=", high))
}
if (add_back) {
args <- paste(args, "--add_back")
}
if (!is.null(wd)) {
args <- paste(args, paste0("--wd=", wd))
}
if (compress_rasters) {
args <- paste(args, "--compress_rasters")
}
tool_name <- "canny_edge_detection"
wbt_run_tool(tool_name, args, verbose_mode)
}
wbt_conservative_smoothing_filter <- function(input, output, filterx=3, filtery=3, wd=NULL, verbose_mode=FALSE, compress_rasters=FALSE) {
wbt_init()
args <- ""
args <- paste(args, paste0("--input=", input))
args <- paste(args, paste0("--output=", output))
if (!is.null(filterx)) {
args <- paste(args, paste0("--filterx=", filterx))
}
if (!is.null(filtery)) {
args <- paste(args, paste0("--filtery=", filtery))
}
if (!is.null(wd)) {
args <- paste(args, paste0("--wd=", wd))
}
if (compress_rasters) {
args <- paste(args, "--compress_rasters")
}
tool_name <- "conservative_smoothing_filter"
wbt_run_tool(tool_name, args, verbose_mode)
}
wbt_corner_detection <- function(input, output, wd=NULL, verbose_mode=FALSE, compress_rasters=FALSE) {
wbt_init()
args <- ""
args <- paste(args, paste0("--input=", input))
args <- paste(args, paste0("--output=", output))
if (!is.null(wd)) {
args <- paste(args, paste0("--wd=", wd))
}
if (compress_rasters) {
args <- paste(args, "--compress_rasters")
}
tool_name <- "corner_detection"
wbt_run_tool(tool_name, args, verbose_mode)
}
wbt_diff_of_gaussian_filter <- function(input, output, sigma1=2.0, sigma2=4.0, wd=NULL, verbose_mode=FALSE, compress_rasters=FALSE) {
wbt_init()
args <- ""
args <- paste(args, paste0("--input=", input))
args <- paste(args, paste0("--output=", output))
if (!is.null(sigma1)) {
args <- paste(args, paste0("--sigma1=", sigma1))
}
if (!is.null(sigma2)) {
args <- paste(args, paste0("--sigma2=", sigma2))
}
if (!is.null(wd)) {
args <- paste(args, paste0("--wd=", wd))
}
if (compress_rasters) {
args <- paste(args, "--compress_rasters")
}
tool_name <- "diff_of_gaussian_filter"
wbt_run_tool(tool_name, args, verbose_mode)
}
wbt_diversity_filter <- function(input, output, filterx=11, filtery=11, wd=NULL, verbose_mode=FALSE, compress_rasters=FALSE) {
wbt_init()
args <- ""
args <- paste(args, paste0("--input=", input))
args <- paste(args, paste0("--output=", output))
if (!is.null(filterx)) {
args <- paste(args, paste0("--filterx=", filterx))
}
if (!is.null(filtery)) {
args <- paste(args, paste0("--filtery=", filtery))
}
if (!is.null(wd)) {
args <- paste(args, paste0("--wd=", wd))
}
if (compress_rasters) {
args <- paste(args, "--compress_rasters")
}
tool_name <- "diversity_filter"
wbt_run_tool(tool_name, args, verbose_mode)
}
wbt_edge_preserving_mean_filter <- function(input, output, threshold, filter=11, wd=NULL, verbose_mode=FALSE, compress_rasters=FALSE) {
wbt_init()
args <- ""
args <- paste(args, paste0("--input=", input))
args <- paste(args, paste0("--output=", output))
args <- paste(args, paste0("--threshold=", threshold))
if (!is.null(filter)) {
args <- paste(args, paste0("--filter=", filter))
}
if (!is.null(wd)) {
args <- paste(args, paste0("--wd=", wd))
}
if (compress_rasters) {
args <- paste(args, "--compress_rasters")
}
tool_name <- "edge_preserving_mean_filter"
wbt_run_tool(tool_name, args, verbose_mode)
}
wbt_emboss_filter <- function(input, output, direction="n", clip=0.0, wd=NULL, verbose_mode=FALSE, compress_rasters=FALSE) {
wbt_init()
args <- ""
args <- paste(args, paste0("--input=", input))
args <- paste(args, paste0("--output=", output))
if (!is.null(direction)) {
args <- paste(args, paste0("--direction=", direction))
}
if (!is.null(clip)) {
args <- paste(args, paste0("--clip=", clip))
}
if (!is.null(wd)) {
args <- paste(args, paste0("--wd=", wd))
}
if (compress_rasters) {
args <- paste(args, "--compress_rasters")
}
tool_name <- "emboss_filter"
wbt_run_tool(tool_name, args, verbose_mode)
}
wbt_fast_almost_gaussian_filter <- function(input, output, sigma=1.8, wd=NULL, verbose_mode=FALSE, compress_rasters=FALSE) {
wbt_init()
args <- ""
args <- paste(args, paste0("--input=", input))
args <- paste(args, paste0("--output=", output))
if (!is.null(sigma)) {
args <- paste(args, paste0("--sigma=", sigma))
}
if (!is.null(wd)) {
args <- paste(args, paste0("--wd=", wd))
}
if (compress_rasters) {
args <- paste(args, "--compress_rasters")
}
tool_name <- "fast_almost_gaussian_filter"
wbt_run_tool(tool_name, args, verbose_mode)
}
wbt_gaussian_filter <- function(input, output, sigma=0.75, wd=NULL, verbose_mode=FALSE, compress_rasters=FALSE) {
wbt_init()
args <- ""
args <- paste(args, paste0("--input=", input))
args <- paste(args, paste0("--output=", output))
if (!is.null(sigma)) {
args <- paste(args, paste0("--sigma=", sigma))
}
if (!is.null(wd)) {
args <- paste(args, paste0("--wd=", wd))
}
if (compress_rasters) {
args <- paste(args, "--compress_rasters")
}
tool_name <- "gaussian_filter"
wbt_run_tool(tool_name, args, verbose_mode)
}
wbt_high_pass_filter <- function(input, output, filterx=11, filtery=11, wd=NULL, verbose_mode=FALSE, compress_rasters=FALSE) {
wbt_init()
args <- ""
args <- paste(args, paste0("--input=", input))
args <- paste(args, paste0("--output=", output))
if (!is.null(filterx)) {
args <- paste(args, paste0("--filterx=", filterx))
}
if (!is.null(filtery)) {
args <- paste(args, paste0("--filtery=", filtery))
}
if (!is.null(wd)) {
args <- paste(args, paste0("--wd=", wd))
}
if (compress_rasters) {
args <- paste(args, "--compress_rasters")
}
tool_name <- "high_pass_filter"
wbt_run_tool(tool_name, args, verbose_mode)
}
wbt_high_pass_median_filter <- function(input, output, filterx=11, filtery=11, sig_digits=2, wd=NULL, verbose_mode=FALSE, compress_rasters=FALSE) {
wbt_init()
args <- ""
args <- paste(args, paste0("--input=", input))
args <- paste(args, paste0("--output=", output))
if (!is.null(filterx)) {
args <- paste(args, paste0("--filterx=", filterx))
}
if (!is.null(filtery)) {
args <- paste(args, paste0("--filtery=", filtery))
}
if (!is.null(sig_digits)) {
args <- paste(args, paste0("--sig_digits=", sig_digits))
}
if (!is.null(wd)) {
args <- paste(args, paste0("--wd=", wd))
}
if (compress_rasters) {
args <- paste(args, "--compress_rasters")
}
tool_name <- "high_pass_median_filter"
wbt_run_tool(tool_name, args, verbose_mode)
}
wbt_k_nearest_mean_filter <- function(input, output, filterx=11, filtery=11, k=5, wd=NULL, verbose_mode=FALSE, compress_rasters=FALSE) {
wbt_init()
args <- ""
args <- paste(args, paste0("--input=", input))
args <- paste(args, paste0("--output=", output))
if (!is.null(filterx)) {
args <- paste(args, paste0("--filterx=", filterx))
}
if (!is.null(filtery)) {
args <- paste(args, paste0("--filtery=", filtery))
}
if (!is.null(k)) {
args <- paste(args, paste0("--k=", k))
}
if (!is.null(wd)) {
args <- paste(args, paste0("--wd=", wd))
}
if (compress_rasters) {
args <- paste(args, "--compress_rasters")
}
tool_name <- "k_nearest_mean_filter"
wbt_run_tool(tool_name, args, verbose_mode)
}
wbt_laplacian_filter <- function(input, output, variant="3x3(1)", clip=0.0, wd=NULL, verbose_mode=FALSE, compress_rasters=FALSE) {
wbt_init()
args <- ""
args <- paste(args, paste0("--input=", input))
args <- paste(args, paste0("--output=", output))
if (!is.null(variant)) {
args <- paste(args, paste0("--variant=", variant))
}
if (!is.null(clip)) {
args <- paste(args, paste0("--clip=", clip))
}
if (!is.null(wd)) {
args <- paste(args, paste0("--wd=", wd))
}
if (compress_rasters) {
args <- paste(args, "--compress_rasters")
}
tool_name <- "laplacian_filter"
wbt_run_tool(tool_name, args, verbose_mode)
}
wbt_laplacian_of_gaussian_filter <- function(input, output, sigma=0.75, wd=NULL, verbose_mode=FALSE, compress_rasters=FALSE) {
wbt_init()
args <- ""
args <- paste(args, paste0("--input=", input))
args <- paste(args, paste0("--output=", output))
if (!is.null(sigma)) {
args <- paste(args, paste0("--sigma=", sigma))
}
if (!is.null(wd)) {
args <- paste(args, paste0("--wd=", wd))
}
if (compress_rasters) {
args <- paste(args, "--compress_rasters")
}
tool_name <- "laplacian_of_gaussian_filter"
wbt_run_tool(tool_name, args, verbose_mode)
}
wbt_lee_sigma_filter <- function(input, output, filterx=11, filtery=11, sigma=10.0, m=5.0, wd=NULL, verbose_mode=FALSE, compress_rasters=FALSE) {
wbt_init()
args <- ""
args <- paste(args, paste0("--input=", input))
args <- paste(args, paste0("--output=", output))
if (!is.null(filterx)) {
args <- paste(args, paste0("--filterx=", filterx))
}
if (!is.null(filtery)) {
args <- paste(args, paste0("--filtery=", filtery))
}
if (!is.null(sigma)) {
args <- paste(args, paste0("--sigma=", sigma))
}
if (!is.null(m)) {
args <- paste(args, paste0("--m=", m))
}
if (!is.null(wd)) {
args <- paste(args, paste0("--wd=", wd))
}
if (compress_rasters) {
args <- paste(args, "--compress_rasters")
}
tool_name <- "lee_sigma_filter"
wbt_run_tool(tool_name, args, verbose_mode)
}
wbt_line_detection_filter <- function(input, output, variant="vertical", absvals=FALSE, clip=0.0, wd=NULL, verbose_mode=FALSE, compress_rasters=FALSE) {
wbt_init()
args <- ""
args <- paste(args, paste0("--input=", input))
args <- paste(args, paste0("--output=", output))
if (!is.null(variant)) {
args <- paste(args, paste0("--variant=", variant))
}
if (absvals) {
args <- paste(args, "--absvals")
}
if (!is.null(clip)) {
args <- paste(args, paste0("--clip=", clip))
}
if (!is.null(wd)) {
args <- paste(args, paste0("--wd=", wd))
}
if (compress_rasters) {
args <- paste(args, "--compress_rasters")
}
tool_name <- "line_detection_filter"
wbt_run_tool(tool_name, args, verbose_mode)
}
wbt_majority_filter <- function(input, output, filterx=11, filtery=11, wd=NULL, verbose_mode=FALSE, compress_rasters=FALSE) {
wbt_init()
args <- ""
args <- paste(args, paste0("--input=", input))
args <- paste(args, paste0("--output=", output))
if (!is.null(filterx)) {
args <- paste(args, paste0("--filterx=", filterx))
}
if (!is.null(filtery)) {
args <- paste(args, paste0("--filtery=", filtery))
}
if (!is.null(wd)) {
args <- paste(args, paste0("--wd=", wd))
}
if (compress_rasters) {
args <- paste(args, "--compress_rasters")
}
tool_name <- "majority_filter"
wbt_run_tool(tool_name, args, verbose_mode)
}
wbt_maximum_filter <- function(input, output, filterx=11, filtery=11, wd=NULL, verbose_mode=FALSE, compress_rasters=FALSE) {
wbt_init()
args <- ""
args <- paste(args, paste0("--input=", input))
args <- paste(args, paste0("--output=", output))
if (!is.null(filterx)) {
args <- paste(args, paste0("--filterx=", filterx))
}
if (!is.null(filtery)) {
args <- paste(args, paste0("--filtery=", filtery))
}
if (!is.null(wd)) {
args <- paste(args, paste0("--wd=", wd))
}
if (compress_rasters) {
args <- paste(args, "--compress_rasters")
}
tool_name <- "maximum_filter"
wbt_run_tool(tool_name, args, verbose_mode)
}
wbt_mean_filter <- function(input, output, filterx=3, filtery=3, wd=NULL, verbose_mode=FALSE, compress_rasters=FALSE) {
wbt_init()
args <- ""
args <- paste(args, paste0("--input=", input))
args <- paste(args, paste0("--output=", output))
if (!is.null(filterx)) {
args <- paste(args, paste0("--filterx=", filterx))
}
if (!is.null(filtery)) {
args <- paste(args, paste0("--filtery=", filtery))
}
if (!is.null(wd)) {
args <- paste(args, paste0("--wd=", wd))
}
if (compress_rasters) {
args <- paste(args, "--compress_rasters")
}
tool_name <- "mean_filter"
wbt_run_tool(tool_name, args, verbose_mode)
}
wbt_median_filter <- function(input, output, filterx=11, filtery=11, sig_digits=2, wd=NULL, verbose_mode=FALSE, compress_rasters=FALSE) {
wbt_init()
args <- ""
args <- paste(args, paste0("--input=", input))
args <- paste(args, paste0("--output=", output))
if (!is.null(filterx)) {
args <- paste(args, paste0("--filterx=", filterx))
}
if (!is.null(filtery)) {
args <- paste(args, paste0("--filtery=", filtery))
}
if (!is.null(sig_digits)) {
args <- paste(args, paste0("--sig_digits=", sig_digits))
}
if (!is.null(wd)) {
args <- paste(args, paste0("--wd=", wd))
}
if (compress_rasters) {
args <- paste(args, "--compress_rasters")
}
tool_name <- "median_filter"
wbt_run_tool(tool_name, args, verbose_mode)
}
wbt_minimum_filter <- function(input, output, filterx=11, filtery=11, wd=NULL, verbose_mode=FALSE, compress_rasters=FALSE) {
wbt_init()
args <- ""
args <- paste(args, paste0("--input=", input))
args <- paste(args, paste0("--output=", output))
if (!is.null(filterx)) {
args <- paste(args, paste0("--filterx=", filterx))
}
if (!is.null(filtery)) {
args <- paste(args, paste0("--filtery=", filtery))
}
if (!is.null(wd)) {
args <- paste(args, paste0("--wd=", wd))
}
if (compress_rasters) {
args <- paste(args, "--compress_rasters")
}
tool_name <- "minimum_filter"
wbt_run_tool(tool_name, args, verbose_mode)
}
wbt_olympic_filter <- function(input, output, filterx=11, filtery=11, wd=NULL, verbose_mode=FALSE, compress_rasters=FALSE) {
wbt_init()
args <- ""
args <- paste(args, paste0("--input=", input))
args <- paste(args, paste0("--output=", output))
if (!is.null(filterx)) {
args <- paste(args, paste0("--filterx=", filterx))
}
if (!is.null(filtery)) {
args <- paste(args, paste0("--filtery=", filtery))
}
if (!is.null(wd)) {
args <- paste(args, paste0("--wd=", wd))
}
if (compress_rasters) {
args <- paste(args, "--compress_rasters")
}
tool_name <- "olympic_filter"
wbt_run_tool(tool_name, args, verbose_mode)
}
wbt_percentile_filter <- function(input, output, filterx=11, filtery=11, sig_digits=2, wd=NULL, verbose_mode=FALSE, compress_rasters=FALSE) {
wbt_init()
args <- ""
args <- paste(args, paste0("--input=", input))
args <- paste(args, paste0("--output=", output))
if (!is.null(filterx)) {
args <- paste(args, paste0("--filterx=", filterx))
}
if (!is.null(filtery)) {
args <- paste(args, paste0("--filtery=", filtery))
}
if (!is.null(sig_digits)) {
args <- paste(args, paste0("--sig_digits=", sig_digits))
}
if (!is.null(wd)) {
args <- paste(args, paste0("--wd=", wd))
}
if (compress_rasters) {
args <- paste(args, "--compress_rasters")
}
tool_name <- "percentile_filter"
wbt_run_tool(tool_name, args, verbose_mode)
}
wbt_prewitt_filter <- function(input, output, clip=0.0, wd=NULL, verbose_mode=FALSE, compress_rasters=FALSE) {
wbt_init()
args <- ""
args <- paste(args, paste0("--input=", input))
args <- paste(args, paste0("--output=", output))
if (!is.null(clip)) {
args <- paste(args, paste0("--clip=", clip))
}
if (!is.null(wd)) {
args <- paste(args, paste0("--wd=", wd))
}
if (compress_rasters) {
args <- paste(args, "--compress_rasters")
}
tool_name <- "prewitt_filter"
wbt_run_tool(tool_name, args, verbose_mode)
}
wbt_range_filter <- function(input, output, filterx=11, filtery=11, wd=NULL, verbose_mode=FALSE, compress_rasters=FALSE) {
wbt_init()
args <- ""
args <- paste(args, paste0("--input=", input))
args <- paste(args, paste0("--output=", output))
if (!is.null(filterx)) {
args <- paste(args, paste0("--filterx=", filterx))
}
if (!is.null(filtery)) {
args <- paste(args, paste0("--filtery=", filtery))
}
if (!is.null(wd)) {
args <- paste(args, paste0("--wd=", wd))
}
if (compress_rasters) {
args <- paste(args, "--compress_rasters")
}
tool_name <- "range_filter"
wbt_run_tool(tool_name, args, verbose_mode)
}
wbt_roberts_cross_filter <- function(input, output, clip=0.0, wd=NULL, verbose_mode=FALSE, compress_rasters=FALSE) {
wbt_init()
args <- ""
args <- paste(args, paste0("--input=", input))
args <- paste(args, paste0("--output=", output))
if (!is.null(clip)) {
args <- paste(args, paste0("--clip=", clip))
}
if (!is.null(wd)) {
args <- paste(args, paste0("--wd=", wd))
}
if (compress_rasters) {
args <- paste(args, "--compress_rasters")
}
tool_name <- "roberts_cross_filter"
wbt_run_tool(tool_name, args, verbose_mode)
}
wbt_scharr_filter <- function(input, output, clip=0.0, wd=NULL, verbose_mode=FALSE, compress_rasters=FALSE) {
wbt_init()
args <- ""
args <- paste(args, paste0("--input=", input))
args <- paste(args, paste0("--output=", output))
if (!is.null(clip)) {
args <- paste(args, paste0("--clip=", clip))
}
if (!is.null(wd)) {
args <- paste(args, paste0("--wd=", wd))
}
if (compress_rasters) {
args <- paste(args, "--compress_rasters")
}
tool_name <- "scharr_filter"
wbt_run_tool(tool_name, args, verbose_mode)
}
wbt_sobel_filter <- function(input, output, variant="3x3", clip=0.0, wd=NULL, verbose_mode=FALSE, compress_rasters=FALSE) {
wbt_init()
args <- ""
args <- paste(args, paste0("--input=", input))
args <- paste(args, paste0("--output=", output))
if (!is.null(variant)) {
args <- paste(args, paste0("--variant=", variant))
}
if (!is.null(clip)) {
args <- paste(args, paste0("--clip=", clip))
}
if (!is.null(wd)) {
args <- paste(args, paste0("--wd=", wd))
}
if (compress_rasters) {
args <- paste(args, "--compress_rasters")
}
tool_name <- "sobel_filter"
wbt_run_tool(tool_name, args, verbose_mode)
}
wbt_standard_deviation_filter <- function(input, output, filterx=11, filtery=11, wd=NULL, verbose_mode=FALSE, compress_rasters=FALSE) {
wbt_init()
args <- ""
args <- paste(args, paste0("--input=", input))
args <- paste(args, paste0("--output=", output))
if (!is.null(filterx)) {
args <- paste(args, paste0("--filterx=", filterx))
}
if (!is.null(filtery)) {
args <- paste(args, paste0("--filtery=", filtery))
}
if (!is.null(wd)) {
args <- paste(args, paste0("--wd=", wd))
}
if (compress_rasters) {
args <- paste(args, "--compress_rasters")
}
tool_name <- "standard_deviation_filter"
wbt_run_tool(tool_name, args, verbose_mode)
}
wbt_total_filter <- function(input, output, filterx=11, filtery=11, wd=NULL, verbose_mode=FALSE, compress_rasters=FALSE) {
wbt_init()
args <- ""
args <- paste(args, paste0("--input=", input))
args <- paste(args, paste0("--output=", output))
if (!is.null(filterx)) {
args <- paste(args, paste0("--filterx=", filterx))
}
if (!is.null(filtery)) {
args <- paste(args, paste0("--filtery=", filtery))
}
if (!is.null(wd)) {
args <- paste(args, paste0("--wd=", wd))
}
if (compress_rasters) {
args <- paste(args, "--compress_rasters")
}
tool_name <- "total_filter"
wbt_run_tool(tool_name, args, verbose_mode)
}
wbt_unsharp_masking <- function(input, output, sigma=0.75, amount=100.0, threshold=0.0, wd=NULL, verbose_mode=FALSE, compress_rasters=FALSE) {
wbt_init()
args <- ""
args <- paste(args, paste0("--input=", input))
args <- paste(args, paste0("--output=", output))
if (!is.null(sigma)) {
args <- paste(args, paste0("--sigma=", sigma))
}
if (!is.null(amount)) {
args <- paste(args, paste0("--amount=", amount))
}
if (!is.null(threshold)) {
args <- paste(args, paste0("--threshold=", threshold))
}
if (!is.null(wd)) {
args <- paste(args, paste0("--wd=", wd))
}
if (compress_rasters) {
args <- paste(args, "--compress_rasters")
}
tool_name <- "unsharp_masking"
wbt_run_tool(tool_name, args, verbose_mode)
}
wbt_user_defined_weights_filter <- function(input, weights, output, center="center", normalize=FALSE, wd=NULL, verbose_mode=FALSE, compress_rasters=FALSE) {
wbt_init()
args <- ""
args <- paste(args, paste0("--input=", input))
args <- paste(args, paste0("--weights=", weights))
args <- paste(args, paste0("--output=", output))
if (!is.null(center)) {
args <- paste(args, paste0("--center=", center))
}
if (normalize) {
args <- paste(args, "--normalize")
}
if (!is.null(wd)) {
args <- paste(args, paste0("--wd=", wd))
}
if (compress_rasters) {
args <- paste(args, "--compress_rasters")
}
tool_name <- "user_defined_weights_filter"
wbt_run_tool(tool_name, args, verbose_mode)
}
wbt_balance_contrast_enhancement <- function(input, output, band_mean=100.0, wd=NULL, verbose_mode=FALSE, compress_rasters=FALSE) {
wbt_init()
args <- ""
args <- paste(args, paste0("--input=", input))
args <- paste(args, paste0("--output=", output))
if (!is.null(band_mean)) {
args <- paste(args, paste0("--band_mean=", band_mean))
}
if (!is.null(wd)) {
args <- paste(args, paste0("--wd=", wd))
}
if (compress_rasters) {
args <- paste(args, "--compress_rasters")
}
tool_name <- "balance_contrast_enhancement"
wbt_run_tool(tool_name, args, verbose_mode)
}
wbt_correct_vignetting <- function(input, pp, output, focal_length=304.8, image_width=228.6, n=4.0, wd=NULL, verbose_mode=FALSE, compress_rasters=FALSE) {
wbt_init()
args <- ""
args <- paste(args, paste0("--input=", input))
args <- paste(args, paste0("--pp=", pp))
args <- paste(args, paste0("--output=", output))
if (!is.null(focal_length)) {
args <- paste(args, paste0("--focal_length=", focal_length))
}
if (!is.null(image_width)) {
args <- paste(args, paste0("--image_width=", image_width))
}
if (!is.null(n)) {
args <- paste(args, paste0("--n=", n))
}
if (!is.null(wd)) {
args <- paste(args, paste0("--wd=", wd))
}
if (compress_rasters) {
args <- paste(args, "--compress_rasters")
}
tool_name <- "correct_vignetting"
wbt_run_tool(tool_name, args, verbose_mode)
}
wbt_direct_decorrelation_stretch <- function(input, output, k=0.5, clip=1.0, wd=NULL, verbose_mode=FALSE, compress_rasters=FALSE) {
wbt_init()
args <- ""
args <- paste(args, paste0("--input=", input))
args <- paste(args, paste0("--output=", output))
if (!is.null(k)) {
args <- paste(args, paste0("--k=", k))
}
if (!is.null(clip)) {
args <- paste(args, paste0("--clip=", clip))
}
if (!is.null(wd)) {
args <- paste(args, paste0("--wd=", wd))
}
if (compress_rasters) {
args <- paste(args, "--compress_rasters")
}
tool_name <- "direct_decorrelation_stretch"
wbt_run_tool(tool_name, args, verbose_mode)
}
wbt_gamma_correction <- function(input, output, gamma=0.5, wd=NULL, verbose_mode=FALSE, compress_rasters=FALSE) {
wbt_init()
args <- ""
args <- paste(args, paste0("--input=", input))
args <- paste(args, paste0("--output=", output))
if (!is.null(gamma)) {
args <- paste(args, paste0("--gamma=", gamma))
}
if (!is.null(wd)) {
args <- paste(args, paste0("--wd=", wd))
}
if (compress_rasters) {
args <- paste(args, "--compress_rasters")
}
tool_name <- "gamma_correction"
wbt_run_tool(tool_name, args, verbose_mode)
}
wbt_gaussian_contrast_stretch <- function(input, output, num_tones=256, wd=NULL, verbose_mode=FALSE, compress_rasters=FALSE) {
wbt_init()
args <- ""
args <- paste(args, paste0("--input=", input))
args <- paste(args, paste0("--output=", output))
if (!is.null(num_tones)) {
args <- paste(args, paste0("--num_tones=", num_tones))
}
if (!is.null(wd)) {
args <- paste(args, paste0("--wd=", wd))
}
if (compress_rasters) {
args <- paste(args, "--compress_rasters")
}
tool_name <- "gaussian_contrast_stretch"
wbt_run_tool(tool_name, args, verbose_mode)
}
wbt_histogram_equalization <- function(input, output, num_tones=256, wd=NULL, verbose_mode=FALSE, compress_rasters=FALSE) {
wbt_init()
args <- ""
args <- paste(args, paste0("--input=", input))
args <- paste(args, paste0("--output=", output))
if (!is.null(num_tones)) {
args <- paste(args, paste0("--num_tones=", num_tones))
}
if (!is.null(wd)) {
args <- paste(args, paste0("--wd=", wd))
}
if (compress_rasters) {
args <- paste(args, "--compress_rasters")
}
tool_name <- "histogram_equalization"
wbt_run_tool(tool_name, args, verbose_mode)
}
wbt_histogram_matching <- function(input, histo_file, output, wd=NULL, verbose_mode=FALSE, compress_rasters=FALSE) {
wbt_init()
args <- ""
args <- paste(args, paste0("--input=", input))
args <- paste(args, paste0("--histo_file=", histo_file))
args <- paste(args, paste0("--output=", output))
if (!is.null(wd)) {
args <- paste(args, paste0("--wd=", wd))
}
if (compress_rasters) {
args <- paste(args, "--compress_rasters")
}
tool_name <- "histogram_matching"
wbt_run_tool(tool_name, args, verbose_mode)
}
wbt_histogram_matching_two_images <- function(input1, input2, output, wd=NULL, verbose_mode=FALSE, compress_rasters=FALSE) {
wbt_init()
args <- ""
args <- paste(args, paste0("--input1=", input1))
args <- paste(args, paste0("--input2=", input2))
args <- paste(args, paste0("--output=", output))
if (!is.null(wd)) {
args <- paste(args, paste0("--wd=", wd))
}
if (compress_rasters) {
args <- paste(args, "--compress_rasters")
}
tool_name <- "histogram_matching_two_images"
wbt_run_tool(tool_name, args, verbose_mode)
}
wbt_min_max_contrast_stretch <- function(input, output, min_val, max_val, num_tones=256, wd=NULL, verbose_mode=FALSE, compress_rasters=FALSE) {
wbt_init()
args <- ""
args <- paste(args, paste0("--input=", input))
args <- paste(args, paste0("--output=", output))
args <- paste(args, paste0("--min_val=", min_val))
args <- paste(args, paste0("--max_val=", max_val))
if (!is.null(num_tones)) {
args <- paste(args, paste0("--num_tones=", num_tones))
}
if (!is.null(wd)) {
args <- paste(args, paste0("--wd=", wd))
}
if (compress_rasters) {
args <- paste(args, "--compress_rasters")
}
tool_name <- "min_max_contrast_stretch"
wbt_run_tool(tool_name, args, verbose_mode)
}
wbt_panchromatic_sharpening <- function(pan, output, red=NULL, green=NULL, blue=NULL, composite=NULL, method="brovey", wd=NULL, verbose_mode=FALSE, compress_rasters=FALSE) {
wbt_init()
args <- ""
args <- paste(args, paste0("--pan=", pan))
args <- paste(args, paste0("--output=", output))
if (!is.null(red)) {
args <- paste(args, paste0("--red=", red))
}
if (!is.null(green)) {
args <- paste(args, paste0("--green=", green))
}
if (!is.null(blue)) {
args <- paste(args, paste0("--blue=", blue))
}
if (!is.null(composite)) {
args <- paste(args, paste0("--composite=", composite))
}
if (!is.null(method)) {
args <- paste(args, paste0("--method=", method))
}
if (!is.null(wd)) {
args <- paste(args, paste0("--wd=", wd))
}
if (compress_rasters) {
args <- paste(args, "--compress_rasters")
}
tool_name <- "panchromatic_sharpening"
wbt_run_tool(tool_name, args, verbose_mode)
}
wbt_percentage_contrast_stretch <- function(input, output, clip=1.0, tail="both", num_tones=256, wd=NULL, verbose_mode=FALSE, compress_rasters=FALSE) {
wbt_init()
args <- ""
args <- paste(args, paste0("--input=", input))
args <- paste(args, paste0("--output=", output))
if (!is.null(clip)) {
args <- paste(args, paste0("--clip=", clip))
}
if (!is.null(tail)) {
args <- paste(args, paste0("--tail=", tail))
}
if (!is.null(num_tones)) {
args <- paste(args, paste0("--num_tones=", num_tones))
}
if (!is.null(wd)) {
args <- paste(args, paste0("--wd=", wd))
}
if (compress_rasters) {
args <- paste(args, "--compress_rasters")
}
tool_name <- "percentage_contrast_stretch"
wbt_run_tool(tool_name, args, verbose_mode)
}
wbt_sigmoidal_contrast_stretch <- function(input, output, cutoff=0.0, gain=1.0, num_tones=256, wd=NULL, verbose_mode=FALSE, compress_rasters=FALSE) {
wbt_init()
args <- ""
args <- paste(args, paste0("--input=", input))
args <- paste(args, paste0("--output=", output))
if (!is.null(cutoff)) {
args <- paste(args, paste0("--cutoff=", cutoff))
}
if (!is.null(gain)) {
args <- paste(args, paste0("--gain=", gain))
}
if (!is.null(num_tones)) {
args <- paste(args, paste0("--num_tones=", num_tones))
}
if (!is.null(wd)) {
args <- paste(args, paste0("--wd=", wd))
}
if (compress_rasters) {
args <- paste(args, "--compress_rasters")
}
tool_name <- "sigmoidal_contrast_stretch"
wbt_run_tool(tool_name, args, verbose_mode)
}
wbt_standard_deviation_contrast_stretch <- function(input, output, stdev=2.0, num_tones=256, wd=NULL, verbose_mode=FALSE, compress_rasters=FALSE) {
wbt_init()
args <- ""
args <- paste(args, paste0("--input=", input))
args <- paste(args, paste0("--output=", output))
if (!is.null(stdev)) {
args <- paste(args, paste0("--stdev=", stdev))
}
if (!is.null(num_tones)) {
args <- paste(args, paste0("--num_tones=", num_tones))
}
if (!is.null(wd)) {
args <- paste(args, paste0("--wd=", wd))
}
if (compress_rasters) {
args <- paste(args, "--compress_rasters")
}
tool_name <- "standard_deviation_contrast_stretch"
wbt_run_tool(tool_name, args, verbose_mode)
} |
div_adjust <- function(x, t, div,
backward = TRUE,
additive = FALSE) {
if (!is.null(dim(x)))
stop(sQuote("x"), " must be a vector")
valid.t <- t > 1L & t <= length(x)
if (all(!valid.t))
return(x)
if (length(div) == 1L && length(t) > 1L)
div <- rep(div, length(t))
else if (length(div) != length(t))
stop("different lengths for ",
sQuote("div"), " and ", sQuote("t"))
div <- div[valid.t]
t <- t[valid.t]
n <- length(x)
if (anyDuplicated(t)) {
div <- tapply(div, t, sum)
t <- as.numeric(names(div))
}
if (!additive) {
rets1 <- c(1, x[-1L]/x[-n])
rets1[t] <- (x[t] + div)/x[t - 1L]
new.series <- x[1L] * cumprod(rets1)
if (backward)
new.series <- new.series * x[n] / new.series[n]
} else {
dif <- c(0, x[-1L] - x[-n])
dif[t] <- dif[t] + div
new.series <- x[1L] + cumsum(dif)
if (backward)
new.series <- new.series - new.series[n] + x[n]
}
new.series
}
split_adjust <- function(x, t, ratio, backward = TRUE) {
if (!is.null(dim(x)))
stop(sQuote("x"), " must be a vector")
valid.t <- t > 1L & t <= length(x)
if (all(!valid.t))
return(x)
if (length(t) > 1L && length(ratio) == 1L)
ratio <- rep(ratio, length(t))
ratio <- ratio[valid.t]
t <- t[valid.t]
if (length(ratio) != length(t))
stop("different lengths for ", sQuote("ratio"),
" and ", sQuote("t"))
new.series <- x
for (i in seq_along(t)) {
t1 <- seq_len(t[i] - 1L)
new.series[t1] <- new.series[t1]/ratio[i]
}
if (!backward)
new.series <- x[1L] * new.series/new.series[1L]
new.series
} |
get_hyperparameter_defaults <- function(models = get_supported_models(),
n = 100,
k = 10,
model_class = "classification") {
defaults <-
list(
rf = tibble::tibble(
mtry = floor(sqrt(k)),
splitrule = "extratrees",
min.node.size = if (model_class == "classification") 1L else 5L),
xgb = tibble::tibble(
eta = .3,
gamma = 0,
max_depth = 6,
subsample = .7,
colsample_bytree = .8,
min_child_weight = 1,
nrounds = 50
),
glm = tibble::tibble(
alpha = 1,
lambda = 2 ^ seq(-10, 3, len = 10)
)
)
return(defaults[models])
}
get_random_hyperparameters <- function(models = get_supported_models(),
n = 100,
k = 10,
tune_depth = 5,
model_class = "classification") {
replace_ks <- k < tune_depth
grids <- list()
if ("rf" %in% models) {
split_rules <-
if (model_class == "classification" | model_class == "multiclass") {
c("gini", "extratrees")
} else {
c("variance", "extratrees")
}
grids$rf <-
tibble::tibble(
mtry = sample(seq_len(k), tune_depth, TRUE, prob = 1 / seq_len(k)),
splitrule = sample(split_rules, tune_depth, TRUE),
min.node.size = sample(min(n, 20), tune_depth, TRUE)
)
}
if ("xgb" %in% models) {
grids$xgb <-
tibble::tibble(
eta = runif(tune_depth, 0.001, .5),
gamma = runif(tune_depth, 0, 10),
max_depth = sample(10, tune_depth, replace = TRUE),
subsample = runif(tune_depth, .35, 1),
colsample_bytree = runif(tune_depth, .5, .9),
min_child_weight = stats::rexp(tune_depth, .2),
nrounds = sample(25:1000, tune_depth, prob = 1 / (25:1000))
)
}
if ("glm" %in% models) {
grids$glm <-
expand.grid(
alpha = c(0, 1),
lambda = 2 ^ runif(tune_depth, -10, 3)
) %>%
dplyr::arrange(alpha) %>%
tibble::as_tibble()
}
return(grids)
} |
Id <- "$Id: bhpm.cluster.BB.hier3.lev1.convergence.R,v 1.11 2020/03/31 12:42:23 clb13102 Exp clb13102 $"
bhpm.cluster.BB.dep.lev1.convergence.diag <- function(raw, debug_diagnostic = FALSE)
{
c_base = bhpm.cluster.1a.dep.lev1.convergence.diag(raw, debug_diagnostic)
if (is.null(c_base)) {
return(NULL)
}
monitor = raw$monitor
theta_mon = monitor[monitor$variable == "theta",]$monitor
pi_mon = monitor[monitor$variable == "pi",]$monitor
alpha_pi_mon = monitor[monitor$variable == "alpha.pi",]$monitor
beta_pi_mon = monitor[monitor$variable == "beta.pi",]$monitor
theta.trt.grps <- raw$Trt.Grps[ raw$Trt.Grps$param == "theta", ]$Trt.Grp
nchains = raw$chains
if (alpha_pi_mon == 1 && !("alpha.pi" %in% names(raw))) {
message("Missing alpha.pi data")
return(NULL)
}
if (beta_pi_mon == 1 && !("beta.pi" %in% names(raw))) {
message("Missing beta.pi data")
return(NULL)
}
if (pi_mon == 1 && !("pi" %in% names(raw))) {
message("Missing pi data")
return(NULL)
}
if (raw$sim_type == "MH") {
if (alpha_pi_mon == 1 && !("alpha.pi_acc" %in% names(raw))) {
message("Missing beta.pi_acc data")
return(NULL)
}
if (beta_pi_mon == 1 && !("beta.pi_acc" %in% names(raw))) {
message("Missing beta.pi_acc data")
return(NULL)
}
}
else {
if (theta_mon == 1 && !("theta_acc" %in% names(raw))) {
message("Missing theta_acc data")
return(NULL)
}
}
pi_conv = data.frame(Trt.Grp = integer(0), Outcome.Grp = character(0), stat = numeric(0), upper_ci = numeric(0), stringsAsFactors=FALSE)
alpha.pi_conv = data.frame(Trt.Grp = integer(0), stat = numeric(0), upper_ci = numeric(0), stringsAsFactors=FALSE)
beta.pi_conv = data.frame(Trt.Grp = integer(0), stat = numeric(0), upper_ci = numeric(0), stringsAsFactors=FALSE)
type <- NA
if (nchains > 1) {
type = "Gelman-Rubin"
i = 1
if (pi_mon == 1) {
for (b in 1:raw$nOutcome.Grp[i]) {
for (t in 1:(raw$nTreatments - 1)) {
g = M_global$GelmanRubin(raw$pi[, t, b, ], nchains)
row <- data.frame(Trt.Grp = theta.trt.grps[t], Outcome.Grp = raw$Outcome.Grp[i, b], stat = g$psrf[1], upper_ci = g$psrf[2], stringsAsFactors=FALSE)
pi_conv = rbind(pi_conv, row)
}
}
}
if (alpha_pi_mon == 1) {
for (t in 1:(raw$nTreatments - 1)) {
g = M_global$GelmanRubin(raw$alpha.pi[,t,], nchains)
row <- data.frame(Trt.Grp = theta.trt.grps[t], stat = g$psrf[1], upper_ci = g$psrf[2], stringsAsFactors=FALSE)
alpha.pi_conv = rbind(alpha.pi_conv, row)
}
}
if (beta_pi_mon == 1) {
for (t in 1:(raw$nTreatments - 1)) {
g = M_global$GelmanRubin(raw$beta.pi[,t,], nchains)
row <- data.frame(Trt.Grp = theta.trt.grps[t], stat = g$psrf[1], upper_ci = g$psrf[2], stringsAsFactors=FALSE)
beta.pi_conv = rbind(beta.pi_conv, row)
}
}
}
else {
type = "Geweke"
i = 1
if (pi_mon == 1) {
for (b in 1:raw$nOutcome.Grp[i]) {
for (t in 1:(raw$nTreatments - 1)) {
g = M_global$Geweke(raw$pi[1, t, b, ])
row <- data.frame(Trt.Grp = theta.trt.grps[t], Outcome.Grp = raw$Outcome.Grp[i, b], stat = g$z, upper_ci = NA, stringsAsFactors=FALSE)
pi_conv = rbind(pi_conv, row)
}
}
}
if (alpha_pi_mon == 1) {
for (t in 1:(raw$nTreatments - 1)) {
g = M_global$Geweke(raw$alpha.pi[1, t,])
row <- data.frame(Trt.Grp = theta.trt.grps[t], stat = g$z, upper_ci = NA, stringsAsFactors=FALSE)
alpha.pi_conv = rbind(alpha.pi_conv, row)
}
}
if (beta_pi_mon == 1) {
for (t in 1:(raw$nTreatments - 1)) {
g = M_global$Geweke(raw$beta.pi[1, t,])
row <- data.frame(Trt.Grp = theta.trt.grps[t], stat = g$z, upper_ci = NA, stringsAsFactors=FALSE)
beta.pi_conv = rbind(beta.pi_conv, row)
}
}
}
alpha.pi_acc <- data.frame(nchains = numeric(0), Trt.Grp = integer(0))
beta.pi_acc <- data.frame(nchains = numeric(0), Trt.Grp = integer(0))
theta_acc = data.frame(chain = numeric(0), Trt.Grp = integer(0), Cluster = character(0), Outcome.Grp = character(0),
Outcome = character(0), rate = numeric(0), stringsAsFactors=FALSE)
for (i in 1:raw$nClusters) {
for (b in 1:raw$nOutcome.Grp[i]) {
for (j in 1:raw$nOutcome[i, b]) {
for (c in 1:nchains) {
for (t in 1:(raw$nTreatments - 1)) {
rate <- raw$theta_acc[c, t, i, b, j]/raw$iter
row <- data.frame(chain = c, Trt.Grp = theta.trt.grps[t], Cluster = raw$Clusters[i], Outcome.Grp = raw$Outcome.Grp[i, b],
Outcome = raw$Outcome[i, b,j], rate = rate, stringsAsFactors=FALSE)
theta_acc = rbind(theta_acc, row)
}
}
}
}
}
if (raw$sim_type == "MH") {
for (c in 1:nchains) {
if (alpha_pi_mon == 1) {
for (t in 1:(raw$nTreatments - 1)) {
alpha.pi_acc[c, t] <- raw$alpha.pi_acc[c, t]/raw$iter
}
}
if (beta_pi_mon == 1) {
for (t in 1:(raw$nTreatments - 1)) {
beta.pi_acc[c, t] <- raw$beta.pi_acc[c, t]/raw$iter
}
}
}
}
rownames(theta_acc) <- NULL
rownames(pi_conv) <- NULL
rownames(alpha.pi_conv) <- NULL
rownames(beta.pi_conv) <- NULL
rownames(alpha.pi_acc) <- NULL
rownames(beta.pi_acc) <- NULL
c_base$theta_acc = theta_acc
c_BB = list(pi.conv.diag = pi_conv, alpha.pi.conv.diag = alpha.pi_conv, beta.pi.conv.diag = beta.pi_conv,
alpha.pi_acc = alpha.pi_acc, beta.pi_acc = beta.pi_acc)
conv.diag = c(c_base, c_BB)
attr(conv.diag, "model") = attr(raw, "model")
return(conv.diag)
}
bhpm.cluster.BB.dep.lev1.print.convergence.summary <- function(conv) {
if (is.null(conv)) {
message("NULL conv data")
return(NULL)
}
monitor = conv$monitor
theta_mon = monitor[monitor$variable == "theta",]$monitor
gamma_mon = monitor[monitor$variable == "gamma",]$monitor
mu.theta_mon = monitor[monitor$variable == "mu.theta",]$monitor
mu.gamma_mon = monitor[monitor$variable == "mu.gamma",]$monitor
sigma2.theta_mon = monitor[monitor$variable == "sigma2.theta",]$monitor
sigma2.gamma_mon = monitor[monitor$variable == "sigma2.gamma",]$monitor
mu.theta.0_mon = monitor[monitor$variable == "mu.theta.0",]$monitor
mu.gamma.0_mon = monitor[monitor$variable == "mu.gamma.0",]$monitor
tau2.theta.0_mon = monitor[monitor$variable == "tau2.theta.0",]$monitor
tau2.gamma.0_mon = monitor[monitor$variable == "tau2.gamma.0",]$monitor
pi_mon = monitor[monitor$variable == "pi",]$monitor
alpha_pi_mon = monitor[monitor$variable == "alpha.pi",]$monitor
beta_pi_mon = monitor[monitor$variable == "beta.pi",]$monitor
model = attr(conv, "model")
if (is.null(model)) {
message("Convergence model attribute missing")
return(NULL)
}
if (gamma_mon == 1 && !("gamma.conv.diag" %in% names(conv))) {
message("Missing gamma.conv.diag data")
return(NULL)
}
if (theta_mon == 1 && !("theta.conv.diag" %in% names(conv))) {
message("Missing theta.conv.diag data")
return(NULL)
}
if (mu.gamma_mon == 1 && !("mu.gamma.conv.diag" %in% names(conv))) {
message("Missing mu.gamma.conv.diag data")
return(NULL)
}
if (mu.theta_mon == 1 && !("mu.theta.conv.diag" %in% names(conv))) {
message("Missing mu.theta.conv.diag data")
return(NULL)
}
if (sigma2.gamma_mon == 1 && !("sigma2.gamma.conv.diag" %in% names(conv))) {
message("Missing sigma2.gamma.conv.diag data")
return(NULL)
}
if (sigma2.theta_mon == 1 && !("sigma2.theta.conv.diag" %in% names(conv))) {
message("Missing sigma2.theta.conv.diag data")
return(NULL)
}
if (mu.gamma.0_mon == 1 && !("mu.gamma.0.conv.diag" %in% names(conv))) {
message("Missing mu.gamma.0.conv.diag data")
return(NULL)
}
if (mu.theta.0_mon == 1 && !("mu.theta.0.conv.diag" %in% names(conv))) {
message("Missing mu.theta.0.conv.diag data")
return(NULL)
}
if (tau2.gamma.0_mon == 1 && !("tau2.gamma.0.conv.diag" %in% names(conv))) {
message("Missing tau2.gamma.0.conv.diag data")
return(NULL)
}
if (tau2.theta.0_mon == 1 && !("tau2.theta.0.conv.diag" %in% names(conv))) {
message("Missing tau2.theta.0.conv.diag data")
return(NULL)
}
if (gamma_mon == 1 && !("gamma_acc" %in% names(conv))) {
message("Missing gamma_acc data")
return(NULL)
}
if (theta_mon == 1 && !("theta_acc" %in% names(conv))) {
message("Missing theta_acc data")
return(NULL)
}
if (pi_mon == 1 && !("pi.conv.diag" %in% names(conv))) {
message("Missing pi.conv.diag data")
return(NULL)
}
if (alpha_pi_mon == 1 && !("alpha.pi.conv.diag" %in% names(conv))) {
message("Missing alpha.pi.conv.diag data")
return(NULL)
}
if (beta_pi_mon == 1 && !("beta.pi.conv.diag" %in% names(conv))) {
message("Missing beta.pi.conv.diag data")
return(NULL)
}
if (alpha_pi_mon == 1 && !("alpha.pi_acc" %in% names(conv))) {
message("Missing alpha.pi_acc data")
return(NULL)
}
if (beta_pi_mon == 1 && !("beta.pi_acc" %in% names(conv))) {
message("Missing beta.pi_acc data")
return(NULL)
}
cat(sprintf("Summary Convergence Diagnostics:\n"))
cat(sprintf("================================\n"))
if (conv$type == "Gelman-Rubin") {
if (theta_mon == 1) {
cat(sprintf("theta:\n"))
cat(sprintf("------\n"))
max_t = head(conv$theta.conv.diag[conv$theta.conv.diag$stat == max(conv$theta.conv.diag$stat),,
drop = FALSE], 1)
cat(sprintf("Max Gelman-Rubin diagnostic (%d %s %s %s): %0.6f\n", max_t$Trt.Grp, max_t$Cluster, max_t$Outcome.Grp, max_t$Outcome, max_t$stat))
min_t = head(conv$theta.conv.diag[conv$theta.conv.diag$stat == min(conv$theta.conv.diag$stat),,
drop = FALSE], 1)
cat(sprintf("Min Gelman-Rubin diagnostic (%d %s, %s %s): %0.6f\n", min_t$Trt.Grp, min_t$Cluster, min_t$Outcome.Grp, min_t$Outcome, min_t$stat))
}
if (gamma_mon == 1) {
cat(sprintf("gamma:\n"))
cat(sprintf("------\n"))
max_t = head(conv$gamma.conv.diag[conv$gamma.conv.diag$stat == max(conv$gamma.conv.diag$stat),,
drop = FALSE], 1)
cat(sprintf("Max Gelman-Rubin diagnostic (%s %s %s): %0.6f\n", max_t$Cluster, max_t$Outcome.Grp, max_t$Outcome, max_t$stat))
min_t = head(conv$gamma.conv.diag[conv$gamma.conv.diag$stat == min(conv$gamma.conv.diag$stat),,
drop = FALSE], 1)
cat(sprintf("Min Gelman-Rubin diagnostic (%s %s %s): %0.6f\n", min_t$Cluster, min_t$Outcome.Grp, min_t$Outcome, min_t$stat))
}
if (mu.gamma_mon == 1) {
cat(sprintf("mu.gamma:\n"))
cat(sprintf("---------\n"))
max_t = head(conv$mu.gamma.conv.diag[conv$mu.gamma.conv.diag$stat
== max(conv$mu.gamma.conv.diag$stat),, drop = FALSE], 1)
cat(sprintf("Max Gelman-Rubin diagnostic (%s): %0.6f\n", max_t$Outcome.Grp, max_t$stat))
min_t = head(conv$mu.gamma.conv.diag[conv$mu.gamma.conv.diag$stat
== min(conv$mu.gamma.conv.diag$stat),, drop = FALSE], 1)
cat(sprintf("Min Gelman-Rubin diagnostic (%s): %0.6f\n", min_t$Outcome.Grp, min_t$stat))
}
if (mu.theta_mon == 1) {
cat(sprintf("mu.theta:\n"))
cat(sprintf("---------\n"))
max_t = head(conv$mu.theta.conv.diag[conv$mu.theta.conv.diag$stat
== max(conv$mu.theta.conv.diag$stat),, drop = FALSE], 1)
cat(sprintf("Max Gelman-Rubin diagnostic (%d %s): %0.6f\n", max_t$Trt.Grp, max_t$Outcome.Grp, max_t$stat))
min_t = head(conv$mu.theta.conv.diag[conv$mu.theta.conv.diag$stat
== min(conv$mu.theta.conv.diag$stat),, drop = FALSE], 1)
cat(sprintf("Min Gelman-Rubin diagnostic (%d %s): %0.6f\n", min_t$Trt.Grp, min_t$Outcome.Grp, min_t$stat))
}
if (sigma2.gamma_mon == 1) {
cat(sprintf("sigma2.gamma:\n"))
cat(sprintf("-------------\n"))
max_t = head(conv$sigma2.gamma.conv.diag[conv$sigma2.gamma.conv.diag$stat
== max(conv$sigma2.gamma.conv.diag$stat),, drop = FALSE], 1)
cat(sprintf("Max Gelman-Rubin diagnostic (%s): %0.6f\n", max_t$Outcome.Grp, max_t$stat))
min_t = head(conv$sigma2.gamma.conv.diag[conv$sigma2.gamma.conv.diag$stat
== min(conv$sigma2.gamma.conv.diag$stat),, drop = FALSE], 1)
cat(sprintf("Min Gelman-Rubin diagnostic (%s): %0.6f\n", min_t$Outcome.Grp, min_t$stat))
}
if (sigma2.theta_mon == 1) {
cat(sprintf("sigma2.theta:\n"))
cat(sprintf("-------------\n"))
max_t = head(conv$sigma2.theta.conv.diag[conv$sigma2.theta.conv.diag$stat
== max(conv$sigma2.theta.conv.diag$stat),, drop = FALSE], 1)
cat(sprintf("Max Gelman-Rubin diagnostic (%d %s): %0.6f\n", max_t$Trt.Grp, max_t$Outcome.Grp, max_t$stat))
min_t = head(conv$sigma2.theta.conv.diag[conv$sigma2.theta.conv.diag$stat
== min(conv$sigma2.theta.conv.diag$stat),, drop = FALSE], 1)
cat(sprintf("Min Gelman-Rubin diagnostic (%d %s): %0.6f\n", min_t$Trt.Grp, min_t$Outcome.Grp, min_t$stat))
}
if (pi_mon == 1) {
cat(sprintf("pi:\n"))
cat(sprintf("---\n"))
max_t = head(conv$pi.conv.diag[conv$pi.conv.diag$stat
== max(conv$pi.conv.diag$stat),, drop = FALSE], 1)
cat(sprintf("Max Gelman-Rubin diagnostic (%d %s): %0.6f\n", max_t$Trt.Grp, max_t$Outcome.Grp, max_t$stat))
min_t = head(conv$pi.conv.diag[conv$pi.conv.diag$stat
== min(conv$pi.conv.diag$stat),, drop = FALSE], 1)
cat(sprintf("Min Gelman-Rubin diagnostic (%d %s): %0.6f\n", min_t$Trt.Grp, min_t$Outcome.Grp, min_t$stat))
}
if (mu.gamma.0_mon == 1) {
cat(sprintf("mu.gamma.0:\n"))
cat(sprintf("-----------\n"))
max_t = head(conv$mu.gamma.0.conv.diag[conv$mu.gamma.0.conv.diag$stat
== max(conv$mu.gamma.0.conv.diag$stat), ], 1)
cat(sprintf("Max Gelman-Rubin diagnostic: %0.6f\n", max_t$stat))
min_t = head(conv$mu.gamma.0.conv.diag[conv$mu.gamma.0.conv.diag$stat
== min(conv$mu.gamma.0.conv.diag$stat), ], 1)
cat(sprintf("Min Gelman-Rubin diagnostic: %0.6f\n", min_t$stat))
}
if (mu.theta.0_mon == 1) {
cat(sprintf("mu.theta.0:\n"))
cat(sprintf("-----------\n"))
max_t = head(conv$mu.theta.0.conv.diag[conv$mu.theta.0.conv.diag$stat
== max(conv$mu.theta.0.conv.diag$stat), ], 1)
cat(sprintf("Max Gelman-Rubin diagnostic: %d %0.6f\n", max_t$Trt.Grp, max_t$stat))
min_t = head(conv$mu.theta.0.conv.diag[conv$mu.theta.0.conv.diag$stat
== min(conv$mu.theta.0.conv.diag$stat), ], 1)
cat(sprintf("Min Gelman-Rubin diagnostic: %d %0.6f\n", min_t$Trt.Grp, min_t$stat))
}
if (tau2.gamma.0_mon == 1) {
cat(sprintf("tau2.gamma.0:\n"))
cat(sprintf("-------------\n"))
max_t = head(conv$tau2.gamma.0.conv.diag[conv$tau2.gamma.0.conv.diag$stat
== max(conv$tau2.gamma.0.conv.diag$stat), ], 1)
cat(sprintf("Max Gelman-Rubin diagnostic: %0.6f\n", max_t$stat))
min_t = head(conv$tau2.gamma.0.conv.diag[conv$tau2.gamma.0.conv.diag$stat
== min(conv$tau2.gamma.0.conv.diag$stat), ], 1)
cat(sprintf("Min Gelman-Rubin diagnostic: %0.6f\n", min_t$stat))
}
if (tau2.theta.0_mon == 1) {
cat(sprintf("tau2.theta.0:\n"))
cat(sprintf("-------------\n"))
max_t = head(conv$tau2.theta.0.conv.diag[conv$tau2.theta.0.conv.diag$stat
== max(conv$tau2.theta.0.conv.diag$stat), ], 1)
cat(sprintf("Max Gelman-Rubin diagnostic: %d %0.6f\n", max_t$Trt.Grp, max_t$stat))
min_t = head(conv$tau2.theta.0.conv.diag[conv$tau2.theta.0.conv.diag$stat
== min(conv$tau2.theta.0.conv.diag$stat), ], 1)
cat(sprintf("Min Gelman-Rubin diagnostic: %d %0.6f\n", min_t$Trt.Grp, min_t$stat))
}
if (alpha_pi_mon == 1) {
cat(sprintf("alpha.pi:\n"))
cat(sprintf("----------\n"))
max_t = head(conv$alpha.pi.conv.diag[conv$alpha.pi.conv.diag$stat
== max(conv$alpha.pi.conv.diag$stat), ], 1)
cat(sprintf("Max Gelman-Rubin diagnostic: %d %0.6f\n", max_t$Trt.Grp, max_t$stat))
min_t = head(conv$alpha.pi.conv.diag[conv$alpha.pi.conv.diag$stat
== min(conv$alpha.pi.conv.diag$stat), ], 1)
cat(sprintf("Min Gelman-Rubin diagnostic: %d %0.6f\n", min_t$Trt.Grp, min_t$stat))
}
if (beta_pi_mon == 1) {
cat(sprintf("beta.pi:\n"))
cat(sprintf("--------\n"))
max_t = head(conv$beta.pi.conv.diag[conv$beta.pi.conv.diag$stat
== max(conv$beta.pi.conv.diag$stat), ], 1)
cat(sprintf("Max Gelman-Rubin diagnostic: %d %0.6f\n", max_t$Trt.Grp, max_t$stat))
min_t = head(conv$beta.pi.conv.diag[conv$beta.pi.conv.diag$stat
== min(conv$beta.pi.conv.diag$stat), ], 1)
cat(sprintf("Min Gelman-Rubin diagnostic: %d %0.6f\n", min_t$Trt.Grp, min_t$stat))
}
}
else {
if (theta_mon == 1) {
cat(sprintf("theta:\n"))
cat(sprintf("------\n"))
max_t = head(conv$theta.conv.diag[conv$theta.conv.diag$stat == max(conv$theta.conv.diag$stat),,
drop = FALSE], 1)
cat(sprintf("Max Geweke statistic (%d %s %s %s): %0.6f (%s)\n", max_t$Trt.Grp, max_t$Cluster, max_t$Outcome.Grp, max_t$Outcome, max_t$stat,
chk_val(max_t$stat)))
min_t = head(conv$theta.conv.diag[conv$theta.conv.diag$stat == min(conv$theta.conv.diag$stat),,
drop = FALSE], 1)
cat(sprintf("Min Geweke statistic (%d %s %s %s): %0.6f (%s)\n", min_t$Trt.Grp, min_t$Cluster, min_t$Outcome.Grp, min_t$Outcome, min_t$stat,
chk_val(min_t$stat)))
}
if (gamma_mon == 1) {
cat(sprintf("gamma:\n"))
cat(sprintf("------\n"))
max_t = head(conv$gamma.conv.diag[conv$gamma.conv.diag$stat == max(conv$gamma.conv.diag$stat),,
drop = FALSE], 1)
cat(sprintf("Max Geweke statistic (%s %s %s): %0.6f (%s)\n", max_t$Cluster, max_t$Outcome.Grp, max_t$Outcome, max_t$stat,
chk_val(max_t$stat)))
min_t = head(conv$gamma.conv.diag[conv$gamma.conv.diag$stat == min(conv$gamma.conv.diag$stat),,
drop = FALSE], 1)
cat(sprintf("Min Geweke statistic (%s %s %s): %0.6f (%s)\n", min_t$Cluster, min_t$Outcome.Grp, min_t$Outcome, min_t$stat,
chk_val(min_t$stat)))
}
if (mu.gamma_mon == 1) {
cat(sprintf("mu.gamma:\n"))
cat(sprintf("---------\n"))
max_t = head(conv$mu.gamma.conv.diag[conv$mu.gamma.conv.diag$stat
== max(conv$mu.gamma.conv.diag$stat),, drop = FALSE], 1)
cat(sprintf("Max Geweke statistic (%s): %0.6f (%s)\n", max_t$Outcome.Grp, max_t$stat,
chk_val(max_t$stat)))
min_t = head(conv$mu.gamma.conv.diag[conv$mu.gamma.conv.diag$stat
== min(conv$mu.gamma.conv.diag$stat),, drop = FALSE], 1)
cat(sprintf("Min Geweke statistic (%s): %0.6f (%s)\n", min_t$Outcome.Grp, min_t$stat,
chk_val(min_t$stat)))
}
if (mu.theta_mon == 1) {
cat(sprintf("mu.theta:\n"))
cat(sprintf("---------\n"))
max_t = head(conv$mu.theta.conv.diag[conv$mu.theta.conv.diag$stat
== max(conv$mu.theta.conv.diag$stat),, drop = FALSE], 1)
cat(sprintf("Max Geweke statistic (%d %s): %0.6f (%s)\n", max_t$Trt.Grp, max_t$Outcome.Grp, max_t$stat,
chk_val(max_t$stat)))
min_t = head(conv$mu.theta.conv.diag[conv$mu.theta.conv.diag$stat
== min(conv$mu.theta.conv.diag$stat),, drop = FALSE], 1)
cat(sprintf("Min Geweke statistic (%d %s): %0.6f (%s)\n", min_t$Trt.Grp, min_t$Outcome.Grp, min_t$stat,
chk_val(min_t$stat)))
}
if (sigma2.gamma_mon == 1) {
cat(sprintf("sigma2.gamma:\n"))
cat(sprintf("-------------\n"))
max_t = head(conv$sigma2.gamma.conv.diag[conv$sigma2.gamma.conv.diag$stat
== max(conv$sigma2.gamma.conv.diag$stat),, drop = FALSE], 1)
cat(sprintf("Max Geweke statistic (%s): %0.6f (%s)\n", max_t$Outcome.Grp, max_t$stat,
chk_val(max_t$stat)))
min_t = head(conv$sigma2.gamma.conv.diag[conv$sigma2.gamma.conv.diag$stat
== min(conv$sigma2.gamma.conv.diag$stat),, drop = FALSE], 1)
cat(sprintf("Min Geweke statistic (%s): %0.6f (%s)\n", min_t$Outcome.Grp, min_t$stat,
chk_val(min_t$stat)))
}
if (sigma2.theta_mon == 1) {
cat(sprintf("sigma2.theta:\n"))
cat(sprintf("-------------\n"))
max_t = head(conv$sigma2.theta.conv.diag[conv$sigma2.theta.conv.diag$stat
== max(conv$sigma2.theta.conv.diag$stat),, drop = FALSE], 1)
cat(sprintf("Max Geweke statistic (%d %s): %0.6f (%s)\n", max_t$Trt.Grp, max_t$Outcome.Grp, max_t$stat,
chk_val(max_t$stat)))
min_t = head(conv$sigma2.theta.conv.diag[conv$sigma2.theta.conv.diag$stat
== min(conv$sigma2.theta.conv.diag$stat),, drop = FALSE], 1)
cat(sprintf("Min Geweke statistic (%d %s): %0.6f (%s)\n", min_t$Trt.Grp, min_t$Outcome.Grp, min_t$stat,
chk_val(min_t$stat)))
}
if (pi_mon == 1) {
cat(sprintf("pi:\n"))
cat(sprintf("---\n"))
max_t = head(conv$pi.conv.diag[conv$pi.conv.diag$stat
== max(conv$pi.conv.diag$stat),, drop = FALSE], 1)
cat(sprintf("Max Geweke statistic (%d %s): %0.6f (%s)\n", max_t$Trt.Grp, max_t$Outcome.Grp, max_t$stat,
chk_val(max_t$stat)))
min_t = head(conv$pi.conv.diag[conv$pi.conv.diag$stat
== min(conv$pi.conv.diag$stat),, drop = FALSE], 1)
cat(sprintf("Min Geweke statistic (%d %s): %0.6f (%s)\n", min_t$Trt.Grp, min_t$Outcome.Grp, min_t$stat,
chk_val(min_t$stat)))
}
if (mu.gamma.0_mon == 1) {
cat(sprintf("mu.gamma.0:\n"))
cat(sprintf("-----------\n"))
max_t = head(conv$mu.gamma.0.conv.diag[conv$mu.gamma.0.conv.diag$stat
== max(conv$mu.gamma.0.conv.diag$stat),, drop = FALSE], 1)
cat(sprintf("Max Geweke statistic: %0.6f (%s)\n", max_t$stat,
chk_val(max_t$stat)))
min_t = head(conv$mu.gamma.0.conv.diag[conv$mu.gamma.0.conv.diag$stat
== min(conv$mu.gamma.0.conv.diag$stat),, drop = FALSE], 1)
cat(sprintf("Min Geweke statistic: %0.6f (%s)\n", min_t$stat,
chk_val(min_t$stat)))
}
if (mu.theta.0_mon == 1) {
cat(sprintf("mu.theta.0:\n"))
cat(sprintf("-----------\n"))
max_t = head(conv$mu.theta.0.conv.diag[conv$mu.theta.0.conv.diag$stat
== max(conv$mu.theta.0.conv.diag$stat),, drop = FALSE], 1)
cat(sprintf("Max Geweke statistic: %d %0.6f (%s)\n", max_t$Trt.Grp, max_t$stat,
chk_val(max_t$stat)))
min_t = head(conv$mu.theta.0.conv.diag[conv$mu.theta.0.conv.diag$stat
== min(conv$mu.theta.0.conv.diag$stat),, drop = FALSE], 1)
cat(sprintf("Min Geweke statistic: %d %0.6f (%s)\n", min_t$Trt.Grp, min_t$stat,
chk_val(min_t$stat)))
}
if (tau2.gamma.0_mon == 1) {
cat(sprintf("tau2.gamma.0:\n"))
cat(sprintf("-------------\n"))
max_t = head(conv$tau2.gamma.0.conv.diag[conv$tau2.gamma.0.conv.diag$stat
== max(conv$tau2.gamma.0.conv.diag$stat),, drop = FALSE], 1)
cat(sprintf("Max Geweke statistic: %0.6f (%s)\n", max_t$stat,
chk_val(max_t$stat)))
min_t = head(conv$tau2.gamma.0.conv.diag[conv$tau2.gamma.0.conv.diag$stat
== min(conv$tau2.gamma.0.conv.diag$stat),, drop = FALSE], 1)
cat(sprintf("Min Geweke statistic: %0.6f (%s)\n", min_t$stat,
chk_val(min_t$stat)))
}
if (tau2.theta.0_mon == 1) {
cat(sprintf("tau2.theta.0:\n"))
cat(sprintf("-------------\n"))
max_t = head(conv$tau2.theta.0.conv.diag[conv$tau2.theta.0.conv.diag$stat
== max(conv$tau2.theta.0.conv.diag$stat),, drop = FALSE], 1)
cat(sprintf("Max Geweke statistic: %d %0.6f (%s)\n", max_t$Trt.Grp, max_t$stat,
chk_val(max_t$stat)))
min_t = head(conv$tau2.theta.0.conv.diag[conv$tau2.theta.0.conv.diag$stat
== min(conv$tau2.theta.0.conv.diag$stat),, drop = FALSE], 1)
cat(sprintf("Min Geweke statistic: %d %0.6f (%s)\n", min_t$Trt.Grp, min_t$stat,
chk_val(min_t$stat)))
}
if (alpha_pi_mon == 1) {
cat(sprintf("alpha.pi:\n"))
cat(sprintf("----------\n"))
max_t = head(conv$alpha.pi.conv.diag[conv$alpha.pi.conv.diag$stat
== max(conv$alpha.pi.conv.diag$stat),, drop = FALSE], 1)
cat(sprintf("Max Geweke statistic: %d %0.6f (%s)\n", max_t$Trt.Grp, max_t$stat,
chk_val(max_t$stat)))
min_t = head(conv$alpha.pi.conv.diag[conv$alpha.pi.conv.diag$stat
== min(conv$alpha.pi.conv.diag$stat),, drop = FALSE], 1)
cat(sprintf("Min Geweke statistic: %d %0.6f (%s)\n", min_t$Trt.Grp, min_t$stat,
chk_val(min_t$stat)))
}
if (beta_pi_mon == 1) {
cat(sprintf("beta.pi:\n"))
cat(sprintf("----------\n"))
max_t = head(conv$beta.pi.conv.diag[conv$beta.pi.conv.diag$stat
== max(conv$beta.pi.conv.diag$stat),, drop = FALSE], 1)
cat(sprintf("Max Geweke statistic: %d %0.6f (%s)\n", max_t$Trt.Grp, max_t$stat,
chk_val(max_t$stat)))
min_t = head(conv$beta.pi.conv.diag[conv$beta.pi.conv.diag$stat
== min(conv$beta.pi.conv.diag$stat),, drop = FALSE], 1)
cat(sprintf("Min Geweke statistic: %d %0.6f (%s)\n", min_t$Trt.Grp, min_t$stat,
chk_val(min_t$stat)))
}
}
if (conv$sim_type == "MH") {
cat("\nSampling Acceptance Rates:\n")
cat("==========================\n")
if (theta_mon == 1) {
cat("theta:\n")
cat("------\n")
print(sprintf("Min: %0.6f, Max: %0.6f", min(conv$theta_acc$rate),
max(conv$theta_acc$rate)))
}
if (gamma_mon == 1) {
cat("gamma:\n")
cat("------\n")
print(sprintf("Min: %0.6f, Max: %0.6f", min(conv$gamma_acc$rate),
max(conv$gamma_acc$rate)))
}
if (alpha_pi_mon == 1) {
cat("alpha.pi:\n")
cat("---------\n")
print(sprintf("Min: %0.6f, Max: %0.6f", min(conv$alpha.pi_acc$rate),
max(conv$alpha.pi_acc$rate)))
}
if (beta_pi_mon == 1) {
cat("beta.pi:\n")
cat("--------\n")
print(sprintf("Min: %0.6f, Max: %0.6f", min(conv$beta.pi_acc$rate),
max(conv$beta.pi_acc$rate)))
}
}
else {
cat("\nSampling Acceptance Rates:\n")
cat("==========================\n")
if (theta_mon == 1) {
cat("theta:\n")
cat("------\n")
print(sprintf("Min: %0.6f, Max: %0.6f", min(conv$theta_acc$rate),
max(conv$theta_acc$rate)))
}
}
} |
avgFDR.p.adjust <- function(pval, t, make.decision=FALSE){
adjcondP <- vector("list", length(pval))
adj.pval <- lapply(pval, FUN = p.adjust, method="BH")
for (i in which(p.adjust(sapply(adj.pval,min),method="BH")<=t) ){
adjcondP[[i]] <- p.adjust(pval[[i]], method = "BH")
}
if (make.decision==FALSE){return(adjcondP)}
else if(make.decision==TRUE){
sig.level=sum(p.adjust(sapply(adj.pval,min),method="BH")<=t)/length(pval)*t
f <- function(x){ifelse(x<=sig.level,"reject","not reject")}
return(list(adjust.p = adjcondP,adjust.sig.level=sig.level,dec.make=lapply(adjcondP, f)))
}
} |
to_mhcnuggets_names <- function(mhcs) {
if (length(mhcs) == 0) {
stop("'mhcs' must have at least one value")
}
mhcnuggets_names <- rep(NA, length(mhcs))
for (i in seq_along(mhcnuggets_names)) {
mhcnuggets_names[i] <- mhcnuggetsr::to_mhcnuggets_name(mhcs[i])
}
mhcnuggets_names
} |
NULL
new_bigfloat <- function(x = character(), cxx = TRUE) {
vec_assert(x, character())
if (cxx) {
c_bigfloat(x)
} else {
new_vctr(x, class = c("bignum_bigfloat", "bignum_vctr"))
}
}
bigfloat <- function(x = character()) {
as_bigfloat(x)
}
as_bigfloat <- function(x) {
UseMethod("as_bigfloat")
}
is_bigfloat <- function(x) {
inherits(x, "bignum_bigfloat")
}
vec_ptype_full.bignum_bigfloat <- function(x, ...) {
"bigfloat"
}
vec_ptype_abbr.bignum_bigfloat <- function(x, ...) {
"bigflt"
}
vec_ptype2.bignum_bigfloat.bignum_bigfloat <- function(x, y, ...) x
vec_ptype2.bignum_bigfloat.logical <- function(x, y, ...) x
vec_ptype2.logical.bignum_bigfloat <- function(x, y, ...) y
vec_ptype2.bignum_bigfloat.integer <- function(x, y, ...) x
vec_ptype2.integer.bignum_bigfloat <- function(x, y, ...) y
vec_ptype2.bignum_bigfloat.double <- function(x, y, ...) x
vec_ptype2.double.bignum_bigfloat <- function(x, y, ...) y
vec_ptype2.bignum_bigfloat.bignum_biginteger <- function(x, y, ...) x
vec_ptype2.bignum_biginteger.bignum_bigfloat <- function(x, y, ...) y
vec_cast.bignum_bigfloat.bignum_bigfloat <- function(x, to, ...) {
x
}
vec_cast.bignum_bigfloat.logical <- function(x, to, ...) {
new_bigfloat(as.character(as.integer(x)))
}
vec_cast.logical.bignum_bigfloat <- function(x, to, ..., x_arg = "", to_arg = "") {
out <- c_bigfloat_to_logical(x)
lossy <- !vec_in(x, vec_c(0, 1, NA_bigfloat_, NaN))
maybe_lossy_cast(out, x, to, lossy, x_arg = x_arg, to_arg = to_arg)
}
vec_cast.bignum_bigfloat.integer <- function(x, to, ...) {
new_bigfloat(as.character(x))
}
vec_cast.integer.bignum_bigfloat <- function(x, to, ..., x_arg = "", to_arg = "") {
out <- c_bigfloat_to_integer(x)
lossy <- xor(is.na(x), is.na(out))
maybe_lossy_cast(out, x, to, lossy, x_arg = x_arg, to_arg = to_arg)
}
vec_cast.bignum_bigfloat.double <- function(x, to, ...) {
new_bigfloat(as.character(x))
}
vec_cast.double.bignum_bigfloat <- function(x, to, ..., x_arg = "", to_arg = "") {
out <- c_bigfloat_to_double(x)
x_loopback <- vec_cast(out, new_bigfloat())
x_na <- is.na(x)
lossy <- (x_loopback != x & !x_na) | xor(x_na, is.na(x_loopback))
maybe_lossy_cast(out, x, to, lossy, x_arg = x_arg, to_arg = to_arg)
}
vec_cast.bignum_bigfloat.bignum_biginteger <- function(x, to, ..., x_arg = "", to_arg = "") {
out <- new_bigfloat(vec_data(x))
x_loopback <- vec_cast(out, new_biginteger())
x_na <- is.na(x)
lossy <- (x_loopback != x & !x_na) | xor(x_na, is.na(x_loopback))
maybe_lossy_cast(out, x, to, lossy, x_arg = x_arg, to_arg = to_arg)
}
vec_cast.bignum_bigfloat.character <- function(x, to, ..., x_arg = "", to_arg = "") {
stop_incompatible_cast(x, to, x_arg = x_arg, to_arg = to_arg)
}
vec_cast.character.bignum_bigfloat <- function(x, to, ..., x_arg = "", to_arg = "") {
stop_incompatible_cast(x, to, x_arg = x_arg, to_arg = to_arg)
}
as.logical.bignum_bigfloat <- function(x, ...) {
warn_on_lossy_cast(vec_cast(x, logical()))
}
as.integer.bignum_bigfloat <- function(x, ...) {
warn_on_lossy_cast(vec_cast(x, integer()))
}
as.double.bignum_bigfloat <- function(x, ...) {
warn_on_lossy_cast(vec_cast(x, double()))
}
as.character.bignum_bigfloat <- function(x, ...) {
format(x)
}
as_bigfloat.default <- function(x) {
warn_on_lossy_cast(vec_cast(x, new_bigfloat()))
}
as_bigfloat.character <- function(x) {
new_bigfloat(x)
}
is.na.bignum_bigfloat <- function(x) {
is.na(c_bigfloat_to_double(x))
} |
library(testthat)
source("utils.R")
invoke_test <- function(fun, expected_res, ...) {
res <- seqR::count_kmers(hash_dim=2,
verbose=FALSE,
...)
expect_matrices_equal(expected_res, as.matrix(res))
}
test_that("(string list) test one sequence with kmer_gaps (1) not positional", {
invoke_test(expected_res=to_matrix(c("a.a_1"=3, "b.b_1"=1, "a.b_1"=1)),
kmer_alphabet=c("a", "b"),
sequences=list(c("a", "a", "a", "b", "a", "b", "a")),
kmer_gaps=c(1),
positional=FALSE,
with_kmer_counts=TRUE)
})
test_that("(string list) test one sequence with kmer_gaps (1) positional", {
invoke_test(expected_res=to_matrix(c("1_a.a_1"=1, "2_a.b_1"=1, "3_a.a_1"=1, "4_b.b_1"=1, "5_a.a_1"=1)),
kmer_alphabet=c("a", "b"),
sequences=list(c("a", "a", "a", "b", "a", "b", "a")),
kmer_gaps=c(1),
positional=TRUE,
with_kmer_counts=TRUE)
})
test_that("(string list) test one sequence with gapps (1,0) not positional", {
invoke_test(expected_res=to_matrix(c("b.b.a_1.0"=1, "a.a.b_1.0"=2, "a.b.a_1.0"=1)),
kmer_alphabet=c("a", "b"),
sequences=list(c("a", "a", "a", "b", "a", "b", "a")),
kmer_gaps=c(1, 0),
positional=FALSE,
with_kmer_counts=TRUE)
})
test_that("(string list) test one sequence with gapps (1,0) not positional, alphabet all", {
invoke_test(expected_res=to_matrix(c("b.b.a_1.0"=1, "a.a.b_1.0"=2, "a.b.a_1.0"=1)),
kmer_alphabet="all",
sequences=list(c("a", "a", "a", "b", "a", "b", "a")),
kmer_gaps=c(1, 0),
positional=FALSE,
with_kmer_counts=TRUE)
})
test_that("(string list) test 2 sequences with kmer_gaps (1,1) positional; some items are not from alphabet", {
sequences <- list(
c("a", "b", "c", "c", "as", "b", "c", "a", "a", "b", "a"),
c("b", "b", "c", "c", "a", "a", "b", "c", "a", "b", "a"))
expectedRes <- matrix(c(
1, 0, 0,
0, 1, 1
), nrow=2, byrow=TRUE)
colnames(expectedRes) <- c("6_b.a.b_1.1", "7_b.a.a_1.1", "5_a.b.a_1.1")
invoke_test(expected_res=expectedRes,
kmer_alphabet=c("a", "b"),
sequences=sequences,
kmer_gaps=c(1,1),
positional=TRUE,
with_kmer_counts=TRUE)
})
test_that("(string list) the k-mer is longer than a sequence", {
sequences <- list(
c("a", "b", "a", "b", "a", "b", "a", "b", "a"),
c("a", "b", "a", "a", "a", "a", "b", "a", "a"))
expectedRes <- matrix(nrow=2, ncol=0)
invoke_test(expected_res=expectedRes,
kmer_alphabet=c("a", "b"),
sequences=sequences,
kmer_gaps=rep(1, 10000),
positional=FALSE,
with_kmer_counts=TRUE)
})
test_that("(string vector) count non positional k-mers (0, 1)", {
sequences <- c("AAAAAC", "AAA", "AAAC")
expected_res <- matrix(c(
2, 1,
0, 0,
0, 1), nrow = 3, byrow=TRUE)
colnames(expected_res) <- c("A.A.A_0.1", "A.A.C_0.1")
invoke_test(expected_res = expected_res,
kmer_alphabet=c("A", "C"),
sequences = sequences,
kmer_gaps = c(0,1),
positional = FALSE,
with_kmer_counts=TRUE)
})
test_that("(string vector) count non positional k-mers (0, 1), alphabet all", {
sequences <- c("AAAAAC", "AAA", "AAAC")
expected_res <- matrix(c(
2, 1,
0, 0,
0, 1), nrow = 3, byrow=TRUE)
colnames(expected_res) <- c("A.A.A_0.1", "A.A.C_0.1")
invoke_test(expected_res = expected_res,
kmer_alphabet="all",
sequences = sequences,
kmer_gaps = c(0,1),
positional = FALSE,
with_kmer_counts=TRUE)
})
test_that("(string vector) count non positional k-mers (0, 1); some items are not allowed", {
sequences <- c("AAAACAAAAC", "AACTAAAA", "AACTAAAAC")
expected_res <- matrix(c(
3, 0, 0,
1, 1, 1,
1, 1, 1
), nrow = 3, byrow=TRUE)
colnames(expected_res) <- c("A.A.A_0.1", "A.A.T_0.1", "T.A.A_0.1")
invoke_test(expected_res = expected_res,
kmer_alphabet=c("A", "T"),
sequences = sequences,
kmer_gaps = c(0, 1),
positional = FALSE,
with_kmer_counts=TRUE)
})
test_that("(string vector) the k-mer is longer than the sequence", {
sequences <- c("AAAACAAAAC", "AACTAAAA", "AACTAAAAC")
expected_res <- matrix(nrow=3, ncol=0)
invoke_test(expected_res = expected_res,
kmer_alphabet=c("A", "T"),
sequences = sequences,
kmer_gaps = rep(1,100),
positional = FALSE,
with_kmer_counts=TRUE)
}) |
all_bills <- function(id = NULL, ordinance = NULL, title = NULL, proposer = NULL,
gazette_from = 'all', gazette_to = 'all',
first_from = 'all', first_to = 'all', second_from = 'all',
second_to = 'all', third_from = 'all', third_to = 'all',
n = 10000, extra_param = NULL, count = FALSE, verbose = TRUE) {
query <- "Vbills?"
filter_args <- {}
if (!is.null(id)) {
filter_args <- c(filter_args, .generate_filter("internal_key", id))
}
if (!is.null(ordinance)) {
ordinance <-.capitalise(ordinance)
filter_args <- c(filter_args, paste0("ordinance_title_eng eq '", ordinance, "'"))
}
if (!is.null(title)) {
title <-.capitalise(title)
filter_args <- c(filter_args, paste0("bill_title_eng eq '", title, "'"))
}
if (!is.null(proposer)) {
proposer <-.capitalise(proposer)
filter_args <- c(filter_args, paste0("proposed_by_eng eq '", proposer, "'"))
}
if (is.null(gazette_from) | is.null(gazette_to)) {
filter_args <- c(filter_args, "bill_gazette_date eq null")
} else if (gazette_from != "all" & gazette_to != "all") {
gazette_from <- as.Date(gazette_from)
gazette_to <- as.Date(gazette_to)
filter_args <- c(filter_args, paste0("bill_gazette_date ge datetime\'", gazette_from,
"\' and bill_gazette_date le datetime\'", gazette_to, "\'"))
} else if (gazette_from != "all") {
gazette_from <- as.Date(gazette_from)
filter_args <- c(filter_args, paste0("bill_gazette_date ge datetime\'", gazette_from, "\'"))
} else if (gazette_to != "all") {
gazette_to <- as.Date(gazette_to)
filter_args <- c(filter_args, paste0("bill_gazette_date le datetime\'", gazette_to, "\'"))
}
if (is.null(first_from) | is.null(first_to)) {
filter_args <- c(filter_args, "first_reading_date eq null")
} else if (first_from != "all" & first_to != "all") {
first_from <- as.Date(first_from)
first_to <- as.Date(first_to)
filter_args <- c(filter_args, paste0("first_reading_date ge datetime\'", first_from,
"\' and first_reading_date le datetime\'", first_to, "\'"))
} else if (first_from != "all") {
first_from <- as.Date(first_from)
filter_args <- c(filter_args, paste0("first_reading_date ge datetime\'", first_from, "\'"))
} else if (first_to != "all") {
first_to <- as.Date(first_to)
filter_args <- c(filter_args, paste0("first_reading_date le datetime\'", first_to, "\'"))
}
if (is.null(second_from) | is.null(second_to)) {
filter_args <- c(filter_args, "second_reading_date eq null")
} else if (second_from != "all" & second_to != "all") {
second_from <- as.Date(second_from)
second_to <- as.Date(second_to)
filter_args <- c(filter_args, paste0("second_reading_date ge datetime\'", second_from,
"\' and second_reading_date le datetime\'", second_to, "\'"))
} else if (second_from != "all") {
second_from <- as.Date(second_from)
filter_args <- c(filter_args, paste0("second_reading_date ge datetime\'", second_from, "\'"))
} else if (second_to != "all") {
second_to <- as.Date(second_to)
filter_args <- c(filter_args, paste0("second_reading_date le datetime\'", second_to, "\'"))
}
if (is.null(third_from) | is.null(third_to)) {
filter_args <- c(filter_args, "third_reading_date eq null")
} else if (third_from != "all" & third_to != "all") {
third_from <- as.Date(third_from)
third_to <- as.Date(third_to)
filter_args <- c(filter_args, paste0("third_reading_date ge datetime\'", third_from,
"\' and third_reading_date le datetime\'", third_to, "\'"))
} else if (third_from != "all") {
third_from <- as.Date(third_from)
filter_args <- c(filter_args, paste0("third_reading_date ge datetime\'", third_from, "\'"))
} else if (third_to != "all") {
third_to <- as.Date(third_to)
filter_args <- c(filter_args, paste0("third_reading_date le datetime\'", third_to, "\'"))
}
if (!is.null(filter_args)) {
query <- paste0(query, "$filter=", paste(filter_args, collapse = " and "))
}
if (!is.null(extra_param)) {
query <- paste0(query, extra_param)
}
df <- legco_api("bill", query, n, count, verbose)
if (!count) {
colnames(df) <-.unify_colnames(colnames(df))
}
df
}
legco_all_bills <- all_bills |
NMADBURL = "https://redcap.ispm.unibe.ch/api/"
PUBLICTOKEN = "4A32EEA60CC5410145152BC48BDC73C1" |
expected <- structure(c(1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L,
4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L,
5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L,
1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L,
2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L,
3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L,
4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L,
5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L,
1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L,
2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L,
3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L,
4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L,
5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L,
1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L,
2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L,
3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L,
4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L,
5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L,
1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L,
2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L,
3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L,
4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L,
5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L,
1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L,
2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L,
3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L,
4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L,
5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L,
1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L,
2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L,
3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L,
4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L,
5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L,
1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L,
2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L,
3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L,
4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L,
5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L,
1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L,
2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L,
3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L,
4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L,
5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L,
1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L,
2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L,
3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L,
4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L,
5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L,
1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L,
2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L,
3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L,
4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L,
5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L,
1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L,
2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L,
3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L,
4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L,
5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L,
1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L,
2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L,
3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L,
4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L,
5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L,
1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L,
2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L,
3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L,
4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L,
5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L,
1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L,
2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L,
3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L,
4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L,
5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L,
1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L,
2L, 3L, 4L, 5L), .Label = c("13", "14", "15", "16", "17"), class = "factor")
test(id=0, code={
argv <- structure(list(.Data = structure(c(1L, 2L, 3L, 4L, 5L, 1L, 2L,
3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L,
4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L,
5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L,
1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L,
2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L,
3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L,
4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L,
5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L,
1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L,
2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L,
3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L,
4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L,
5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L,
1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L,
2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L,
3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L,
4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L,
5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L,
1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L,
2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L,
3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L,
4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L,
5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L,
1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L,
2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L,
3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L,
4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L,
5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L,
1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L,
2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L,
3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L,
4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L,
5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L,
1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L,
2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L,
3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L,
4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L,
5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L,
1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L,
2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L,
3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L,
4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L,
5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L,
1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L,
2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L,
3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L,
4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L,
5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L,
1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L,
2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L,
3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L,
4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L,
5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L,
1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L,
2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L,
3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L,
4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L,
5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L,
1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L,
2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L,
3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L,
4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L,
5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L,
1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L,
2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L,
3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L,
4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L,
5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L,
1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L,
2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L,
3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L,
4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L,
5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L,
1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L), .Label = c("13", "14",
"15", "16", "17"), class = "factor"), .Label = c("13", "14",
"15", "16", "17"), class = "factor"), .Names = c(".Data", ".Label",
"class"))
do.call('structure', argv);
}, o = expected); |
boltDimensions = function(dlog, runQuiet = FALSE)
{
require(sampSurf)
if(!is(dlog, 'downLog'))
stop('must pass a downLog argument!')
taper = dlog@taper
nTaper = nrow(taper)
buttDiam = dlog@buttDiam
topDiam = dlog@topDiam
logLen = dlog@logLen
solidType = dlog@solidType
nSegs = nTaper - 1
boltDim = matrix(NA, nrow=nSegs, ncol=5)
boltDim[, 1] = taper[1:nSegs, 'diameter']
boltDim[, 2] = taper[2:nTaper, 'diameter']
boltDim[, 3] = taper[1:nSegs, 'length']
boltDim[, 4] = taper[2:nTaper, 'length']
boltDim[, 5] = diff(taper$length)
vol = matrix(NA, nrow=nSegs, ncol=1)
biomass = vol
carbon = vol
if(!is.null(solidType))
for(i in seq_len(nSegs))
vol[i, 1] = .StemEnv$wbVolume(buttDiam, topDiam, logLen, solidType, boltDim[i,4]) -
.StemEnv$wbVolume(buttDiam, topDiam, logLen, solidType, boltDim[i,3])
else
vol[, 1] = .StemEnv$SmalianVolume(taper)$boltVol
if(!is.na(dlog@biomass))
biomass[, 1] = vol[,1]*dlog@conversions['volumeToWeight']
if(!is.na(dlog@carbon))
carbon[, 1] = biomass[,1]*dlog@conversions['weightToCarbon']
sa = matrix(NA, nrow=nSegs, ncol=1)
for(i in seq_len(nSegs)) {
if(!is.null(solidType))
sa[i, 1] = .StemEnv$wbSurfaceArea(buttDiam, topDiam, logLen, solidType, boltDim[i,3],
boltDim[i,4] )
else
sa[i, 1] = .StemEnv$splineSurfaceArea(taper, boltDim[i,3], boltDim[i,4] )
}
ca = matrix(NA, nrow=nSegs, ncol=1)
for(i in seq_len(nSegs)) {
if(!is.null(solidType))
ca[i, 1] = .StemEnv$wbCoverageArea(buttDiam, topDiam, logLen, solidType, boltDim[i,3],
boltDim[i,4] )
else
ca[i, 1] = .StemEnv$splineCoverageArea(taper, boltDim[i,3], boltDim[i,4] )
}
df = data.frame(boltDim, vol, sa, ca, biomass, carbon)
colnames(df) = c('botDiam', 'topDiam', 'botLen', 'topLen', 'boltLen',
.StemEnv$puaEstimates[c('volume', 'surfaceArea', 'coverageArea',
'biomass', 'carbon')])
if(!runQuiet) {
sums = colSums(df)
if(dlog@units == 'metric') {
lu = 'meters'
cu = 'cubic meters'
su = 'square meters'
}
else {
lu = 'feet'
cu = 'cubic feet'
su = 'square feet'
}
if(is.null(solidType)) {
fromArea = '(from spline fit)'
fromVol = "(from Smalian's)"
}
else {
fromArea = '(from taper equation)'
fromVol = fromArea
}
cat('\nSummary of bolts in taper data frame...')
.StemEnv$underLine(40,postfix='')
cat('\n Units =', dlog@units)
cat('\n Number of segments =', nSegs)
cat('\n Solid type =', ifelse(is.null(solidType), 'NULL', solidType))
cat('\n Total Length =', sums['boltLen'], lu)
cat('\n Total volume =', sums['volume'], cu, fromVol)
cat('\n Total biomass =', sums['biomass'])
cat('\n Total carbon =', sums['carbon'])
cat('\n Total surface area =', sums['surfaceArea'], su, fromArea)
cat('\n Total coverge area =', sums['coverageArea'], su, fromArea)
cat('\n')
}
return(invisible(df))
} |
as.ckan_user <- function(x, ...) UseMethod("as.ckan_user")
as.ckan_user.character <- function(x, ...) get_user(x, ...)
as.ckan_user.ckan_user <- function(x, ...) x
as.ckan_user.list <- function(x, ...) structure(x, class = "ckan_user")
is.ckan_user <- function(x) inherits(x, "ckan_user")
print.ckan_user <- function(x, ...) {
cat(paste0("<CKAN User> ", x$id), "\n")
cat(" Name: ", x$name, "\n", sep = "")
cat(" Display Name: ", x$display_name, "\n", sep = "")
cat(" Full Name: ", x$fullname, "\n", sep = "")
cat(" No. Packages: ", x$number_created_packages, "\n", sep = "")
cat(" No. Edits: ", x$number_of_edits, "\n", sep = "")
cat(" Created: ", x$created, "\n", sep = "")
}
get_user <- function(id, url = get_default_url(), key = get_default_key(),
...) {
res <- ckan_GET(url, 'user_show', list(id = id), key = key, opts = list(...))
as_ck(jsl(res), "ckan_user")
} |
library(testthat)
test_that("autotest", {
autotest_sdistribution(
sdist = Beta,
pars = list(shape1 = 1, shape2 = 1),
traits = list(
valueSupport = "continuous",
variateForm = "univariate",
type = PosReals$new(zero = TRUE)
),
support = Interval$new(0, 1),
symmetry = "symmetric",
mean = 0.5,
mode = NaN,
median = 0.5,
variance = 1 / 12,
skewness = 0,
exkur = -1.2,
entropy = 0,
pgf = NaN,
pdf = dbeta(1:3, 1, 1),
cdf = pbeta(1:3, 1, 1),
quantile = qbeta(c(0.24, 0.42, 0.5), 1, 1)
)
})
test_that("manual", {
expect_equal(Beta$new(1, 2)$mode(), 0)
expect_equal(Beta$new(2, 1)$mode(), 1)
expect_equal(Beta$new(0.5, 0.5)$mode(), c(0, 1))
expect_equal(Beta$new(0.5, 0.5)$mode(1), 0)
expect_equal(Beta$new(2, 2)$mode(1), 0.5)
expect_false(testSymmetric(Beta$new()$setParameterValue(shape1 = 1, shape2 = 2)))
})
test_that("vector", {
d <- VectorDistribution$new(distribution = "Beta",
params = data.frame(shape1 = 1:2, shape2 = 1))
expect_error(d$mode(), "cannot be")
}) |