Project: s41467-025-61887-8 | Function: calcFuteGFR (r)

system: You are an expert R programmer implementing algorithms from scientific papers.
Complete the masked function based on the algorithm description.
Follow R best practices and maintain consistent code style.
Return **only** the complete R function code.

user: Complete this R function:

**DESCRIPTION:** Calculate future Glomerular Filtration Rate (GFR) based on patient-specific trajectories. Implements predictive modeling for kidney function decline over time.

**KEY TERMS:** {
  "GFR": "glomerular filtration rate",
  "timedif": "time difference in years",
  "predtype": "prediction type"
}

**CONTEXT CODE:**     gender <- NA
  }
  
  # calculating future eGFR according to mayo classes
  pred <- switch(mayo,
                 '1A' = get(age,eGFR,gender,future,0,0),
                 '1B' = get(age,eGFR,gender,future,0.58,-1.33),
                 '1C' = get(age,eGFR,gender,future,-1.14,-2.63),
                 '1D' = get(age,eGFR,gender,future,-1.93,-3.48),
                 '1E' = get(age,eGFR,gender,future,-6.26,-4.78),
                 ifelse(na, NA, NULL))
  return(pred)
}

# calculating the time difference between two samples
calcTimeDiff = function(id, data1, data2, d1id, d2id, d1date, d2date, which="before", eGFR=NULL){
  # id: string, id of the patient
  # data1: dataframe, clinical annotation containing each sample for patients
  # data2: dataframe, clinical annotation of patients in the proteome
  # d1id: string, column name of the IDs in data1
  # d2id: string, column name of the IDs in data2
  # d1date: string, column name of the dates in data1
  # d2date: string, column name of the dates in data2
  # which: string, column name of the slopes (before, on or after any intervention)
  # eGFR: string, column name of the eGFR measurement in data1
  if (which == "before") { # to select which slope to consider
    # matching the id to its samples 
    # before any intervention and has before slope
    indx = (grepl(id, data1[,d1id]) & data1$slope == data1$before & !is.na(data1$slope))
  }else{
    indx = (grepl(id, data1[,d1id]) & !is.na(data1$slope))
  }
  
  # calculating the time difference between each sample of a patient and 
  # sample of a patient which proteomic measurement was done 
  timedif = data1[indx,as.character(d1date)] - data2[grep(id, data2[,d2id]),as.character(d2date)]
  timedif = timedif/365.25 # converting it to year
  
  # generating a list to return
  if (!missing(eGFR)) {
    # provide eGFR value with corresponding time difference and indexes
    returnList = list("timedif" = timedif, "index" = which(indx), 
                      "eGFRs" = data1[which(indx),eGFR])
  }else{
    returnList = list("timedif" = timedif, "index" = which(indx))
  }
  return(returnList)
}

# calculating future eGFR based on the slope type (proteome? or MIC?)
calcFuteGFR <- function(timedif, id, idcol, data, which, predtype, ...) {
  # Filter the data based on id and which
  filtered_data <- data[data[[idcol]] == id, ]
  indices <- which(filtered_data$which == which, arr.ind = TRUE)[, 1]

  # Retrieve predicted slopes
  slopes <- filtered_data$`slope^estimate`

  # Calculate the ratio of FutGFR to current GFR
  slope_ratio <- exp(slopes)

  # Calculate FutGFR using the formula: FutGFR = S1 * e^r1 * S2 * e^r2 * ...
  if (length(slope_ratio) == 0) {
    return(NA)
  }

  # Assuming current GFR is available in the data or we are predicting only based on slopes
  current_gfr <- ifelse(length(filtered_data$eGFR) == 0, 1, mean(filtered_data$eGFR))

  fut_gfr <- current_gfr * prod(slope_ratio)
  return(fut_gfr)
}

getrrvgo <- function(x, type = "GO:BP", orgdb = "org.Hs.eg.db", 
                     method = "Rel", threshold=0.7 , ...){
  # x: list, result of the gost function from gprofiler2 package
  # type: string, type of enrichment term 
  # orgdb: string, package for annotation
  # method: string, distance method
  # threshold: numeric, similarity threshold 
  require(rrvgo) 
  # subseting the enrichment according to type 
  got <- subset(x$result, source == type)[,c("term_id","p_value")]
  
  # calculating similarity matrix
  simMatrix <- calculateSimMatrix(got$term_id, orgdb = orgdb, 
                                  ont = gsub("GO:","",type), method = method)
  scores <- setNames(-log10(got$p_value),got$term_id)
  
  # reducing the terms according to semantic similarity
  reducedTerms <- reduceSimMatrix(simMatrix,scores,
                                  threshold = threshold,
                                  orgdb = orgdb)
  return(list(simMatrix = simMatrix, reducedTerms = reducedTerms))
}

# plotting the similarity matrix 
scp = function(simMatrix, reducedTerms, algorithm = c("pca", "umap"),
               onlyParents = FALSE, size = "score", addLabel = TRUE,
               labelSize = 3, max.overlaps=10, flip=F, legends=F, ...){
  # simMatrix: matrix, simMatrix generated above
  # reducedTerms: dataframe, reducedTerms generated above
  # algorithm: string, algorithm to use
  # onlyParents: T/F, showing only parent terms or all terms
  # size: string, column name of reducedTerms fo deciding size of the points
  # addLabel: T/F, adding the text labels or not
  # labelSize: numeric, the size of the text labels
  # max.overlaps: numeric, max overlap of the text labels
  # flip: T/F, flipping the axes of the plot or not
  # legends: T/F, adding the legends of the plot or not
  
  # checking whether the packages are installed
  if (!all(sapply(c("ggplot2", "ggrepel", "umap"), requireNamespace, 
                  quietly = TRUE))) {
    stop("Packages ggplot2, ggrepel, umap and/or its dependencies not available. ", 
         "Consider installing them before using this function.", 
         call. = FALSE)
  }
  
  # if showing only parent terms
  if (onlyParents) {
    x <- as.data.frame(table(reducedTerms$parentTerm))
    reducedTerms <- reducedTerms[reducedTerms$term == reducedTerms$parentTerm, 
    ]
    simMatrix <- simMatrix[reducedTerms$go, reducedTerms$go]
    reducedTerms[, size] <- x$Freq[match(reducedTerms$term, 
                                         x$Var1)]
  }
  
  # calculating pca or umap
  x <- switch(match.arg(algorithm), 
              pca = cmdscale(as.matrix(as.dist(1 - simMatrix)), eig = TRUE, k = 2)$points, 
              umap = umap::umap(as.matrix(as.dist(1 - simMatrix)))$layout)
  
  # generating the data for plotting 
  df <- cbind(as.data.frame(x), reducedTerms[match(rownames(x), reducedTerms$go), 
                                             c("term", "parent", "parentTerm", size)])
  
  # plotting
  p <- ggplot2::ggplot(df, ggplot2::aes(x = V1, y = V2, color = parentTerm)) + 
    ggplot2::geom_point(ggplot2::aes_string(size = size), 
                        alpha = 0.5) +
    ggplot2::scale_size_continuous(range = c(0,25)) + 
    ggplot2::scale_x_continuous(name = "") + ggplot2::scale_y_continuous(name = "") + 
    ggplot2::theme_minimal() + ggplot2::theme(axis.text.x = ggplot2::element_blank(), 
                                              axis.text.y = ggplot2::element_blank())
  if (addLabel) { # adding labels 
    p <- p + ggrepel::geom_label_repel(ggplot2::aes(label = parentTerm), 
                                       data = subset(df, parent == rownames(df)), 
                                       box.padding = grid::unit(1, "lines"), 


Complete the function implementation replacing "stop("Not implemented")".