| |
|
| |
|
| | setGeneric("postSim",
|
| | function(object, n.sims=1000){
|
| | standardGeneric("postSim")
|
| | }
|
| | )
|
| |
|
| | setClass("postSim",
|
| | slots = c(coef = "matrix",
|
| | sigma = "numeric")
|
| | )
|
| |
|
| | setClass("postSim.polr",
|
| | slots = c(coef = "matrix",
|
| | zeta = "matrix")
|
| | )
|
| |
|
| | setMethod("postSim", signature(object = "lm"),
|
| | function(object, n.sims=1000)
|
| | {
|
| | object.class <- class(object)[[1]]
|
| | summ <- summary (object)
|
| | coef <- summ$coef[,1:2,drop=FALSE]
|
| | dimnames(coef)[[2]] <- c("coef.est","coef.sd")
|
| | sigma.hat <- summ$sigma
|
| | beta.hat <- coef[,1,drop = FALSE]
|
| | V.beta <- summ$cov.unscaled
|
| | n <- summ$df[1] + summ$df[2]
|
| | k <- summ$df[1]
|
| | sigma <- rep (NA, n.sims)
|
| | beta <- array (NA, c(n.sims,k))
|
| | dimnames(beta) <- list (NULL, rownames(beta.hat))
|
| | for (s in 1:n.sims){
|
| | sigma[s] <- sigma.hat*sqrt((n-k)/rchisq(1,n-k))
|
| | beta[s,] <- MASS::mvrnorm (1, beta.hat, V.beta*sigma[s]^2)
|
| | }
|
| |
|
| | ans <- new("postSim",
|
| | coef = beta,
|
| | sigma = sigma)
|
| | return (ans)
|
| | }
|
| | )
|
| |
|
| |
|
| | setMethod("postSim", signature(object = "glm"),
|
| | function(object, n.sims=1000)
|
| | {
|
| | object.class <- class(object)[[1]]
|
| | summ <- summary (object, correlation=TRUE, dispersion = object$dispersion)
|
| | coef <- summ$coef[,1:2,drop=FALSE]
|
| | dimnames(coef)[[2]] <- c("coef.est","coef.sd")
|
| | beta.hat <- coef[,1,drop=FALSE]
|
| | sd.beta <- coef[,2,drop=FALSE]
|
| | corr.beta <- summ$corr
|
| | n <- summ$df[1] + summ$df[2]
|
| | k <- summ$df[1]
|
| | V.beta <- corr.beta * array(sd.beta,c(k,k)) * t(array(sd.beta,c(k,k)))
|
| | beta <- array (NA, c(n.sims,k))
|
| | dimnames(beta) <- list (NULL, dimnames(beta.hat)[[1]])
|
| | for (s in 1:n.sims){
|
| | beta[s,] <- MASS::mvrnorm (1, beta.hat, V.beta)
|
| | }
|
| |
|
| | beta2 <- array (0, c(n.sims,length(coefficients(object))))
|
| | dimnames(beta2) <- list (NULL, names(coefficients(object)))
|
| | beta2[,dimnames(beta2)[[2]]%in%dimnames(beta)[[2]]] <- beta
|
| |
|
| | sigma <- rep (sqrt(summ$dispersion), n.sims)
|
| |
|
| | ans <- new("postSim",
|
| | coef = beta2,
|
| | sigma = sigma)
|
| | return(ans)
|
| | }
|
| | )
|
| |
|
| |
|
| | setMethod("postSim", signature(object = "polr"),
|
| | function(object, n.sims=1000){
|
| | x <- as.matrix(model.matrix(object))
|
| | coefs <- coef(object)
|
| | k <- length(coefs)
|
| | zeta <- object$zeta
|
| | Sigma <- vcov(object)
|
| |
|
| | if(n.sims==1){
|
| | parameters <- t(MASS::mvrnorm(n.sims, c(coefs, zeta), Sigma))
|
| | }else{
|
| | parameters <- MASS::mvrnorm(n.sims, c(coefs, zeta), Sigma)
|
| | }
|
| | ans <- new("postSim.polr",
|
| | coef = parameters[,1:k,drop=FALSE],
|
| | zeta = parameters[,-(1:k),drop=FALSE])
|
| | return(ans)
|
| | }
|
| | )
|
| |
|
| |
|
| | setMethod("postSim", signature(object = "svyglm"),
|
| | function(object, n.sims=1000)
|
| | {
|
| | object.class <- class(object)[[2]]
|
| | summ <- summary (object, correlation=TRUE, dispersion = object$dispersion)
|
| | coef <- summ$coef[,1:2,drop=FALSE]
|
| | dimnames(coef)[[2]] <- c("coef.est","coef.sd")
|
| | beta.hat <- coef[,1,drop=FALSE]
|
| | sd.beta <- coef[,2,drop=FALSE]
|
| | corr.beta <- summ$corr
|
| | n <- summ$df[1] + summ$df[2]
|
| | k <- summ$df[1]
|
| | V.beta <- corr.beta * array(sd.beta,c(k,k)) * t(array(sd.beta,c(k,k)))
|
| | beta <- array (NA, c(n.sims,k))
|
| | dimnames(beta) <- list (NULL, dimnames(beta.hat)[[1]])
|
| | for (s in 1:n.sims){
|
| | beta[s,] <- MASS::mvrnorm (1, beta.hat, V.beta)
|
| | }
|
| | beta2 <- array (0, c(n.sims,length(coefficients(object))))
|
| | dimnames(beta2) <- list (NULL, names(coefficients(object)))
|
| | beta2[,dimnames(beta2)[[2]]%in%dimnames(beta)[[2]]] <- beta
|
| | sigma <- rep (sqrt(summ$dispersion), n.sims)
|
| |
|
| | ans <- new("postSim",
|
| | coef = beta2,
|
| | sigma = sigma)
|
| | return(ans)
|
| | }
|
| | )
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