Functions/multi_impute.R

In mariechion/mi4p: Multiple Imputation for Proteomics

# Function for imputing quantitative datasets.

multi.impute <- function(data, conditions, nb.imp = NULL, method, parallel = F){
  conditions <- as.factor(conditions)
  # Determine the number of imputations to be done
  # Rule of thumb (White et al. 2011) : I = % MV
  if (is.null(nb.imp)) {
    nb.imp <- ceiling(sum(is.na(data))/prod(dim(data))*100)
  }
  if (nb.imp<2) {nb.imp <- 2}
  # Imputation method from mice package
  if (method %in% c("pmm", "midastouch", "sample", "cart", "rf",
                        "mean", "norm", "norm.nob", "norm.boot", "norm.predict")) {
    data.imp <- mice::mice(data, m = nb.imp, method = method, printFlag = FALSE)
    data.imp.list <- mice::complete(data.imp, action = "all")
    data.array <- array(unlist(data.imp.list), 
                        dim = c(nrow(data.imp.list[[1]]), ncol(data.imp.list[[1]]), length(data.imp.list)))
  }
  # Imputation method from imp4p package
  if (method == "RF") {
    if (parallel) {
      data.array <- simplify2array(foreach(iforeach =  1:nb.imp,
                                           .errorhandling = 'remove', .verbose = T) %dopar% 
                                     imp4p::impute.RF(tab = data, conditions = conditions))
    }
    else {
      data.array <- simplify2array(lapply(1:nb.imp, function(aaa) {
        imp4p::impute.RF(tab = data, conditions = conditions)}
      ))
    }
  }
  if (method == "MLE") {
    if (parallel) {
      data.array <- simplify2array(foreach(iforeach =  1:nb.imp,
                                           .errorhandling = 'remove', .verbose = T) %dopar% 
                                     impute.mle(tab = data, conditions = conditions))
    }
    else {
      data.array <- simplify2array(lapply(1:nb.imp, function(aaa) {
        imp4p::impute.mle(tab = data, conditions = conditions)}
      ))
    }
  }
  if (method == "PCA") {
    if (parallel) {
      data.array <- simplify2array(foreach(iforeach =  1:nb.imp,
                                           .errorhandling = 'remove', .verbose = T) %dopar% 
                                     impute.PCA(tab = data, conditions = conditions))
    }
    else {
      data.array <- simplify2array(lapply(1:nb.imp, function(aaa) {
        imp4p::impute.PCA(tab = data, conditions = conditions)}
      ))
    }
  }
  if (method == "SLSA") {
    if (parallel) {
      data.array <- simplify2array(foreach(iforeach =  1:nb.imp,
                                           .errorhandling = 'remove', .verbose = T) %dopar% 
                                     imp4p::impute.slsa(tab = data, conditions = conditions))
    }
    else {
      data.array <- simplify2array(lapply(1:nb.imp, function(aaa) {
        imp4p::impute.slsa(tab = data, conditions = conditions)}
      ))
    }
  }
  # Imputation using kNN
  if (method == "kNN") {
    if (parallel) {
      data.array <- simplify2array(foreach(iforeach =  1:nb.imp,
                                           .errorhandling = 'remove', .verbose = T) %dopar% 
                                     impute::impute.knn(data = as.matrix(data))$data)
    }
    else {
      data.array <- simplify2array(lapply(1:nb.imp, function(aaa) {
        impute::impute.knn(data = as.matrix(data))$data}
      ))
    }
  }
  # Returrn arrray of imputed matrices
  return(data.array)
}