R/knn.intcv.R

In yilinwu123/precision1: PaiREd miCrorna sImulation on Study desIgn for mOlecular classificatioN

#' K-Nearest Neighbors Classifier
#'
#' Build a K-Nearest Neighbors classifier using internal cross validation to choose the turning parameter,
#' with a 5-fold cross validation as default.
#'
#' @references https://topepo.github.io/caret/
#' @param X dataset to be trained. This dataset must have rows as probes and columns as samples.
#' @param y a vector of sample group of each sample for the dataset to be trained.
#' It must have an equal length to the number of samples in \code{X}.
#' @param kfold number of folds. By default, \code{kfold = 5}.
#' @param seed an integer used to initialize a pseudorandom number generator.
#' @return a list of 4 elements:
#' \item{mc}{an internal misclassification error rate}
#' \item{time}{the processing time of performing internal validation with kNN}
#' \item{model}{a kNN classifier}
#' @export
#' @export knn.intcv
#' @import caret
#' @keywords classification
#' @examples
#' set.seed(101)
#' biological.effect <- estimate.biological.effect(uhdata = uhdata.pl)
#' ctrl.genes <- unique(rownames(uhdata.pl))[grep("NC", unique(rownames(uhdata.pl)))]
#' biological.effect.nc <- biological.effect[!rownames(biological.effect)
#'   %in% ctrl.genes, ]
#' group.id <- substr(colnames(biological.effect.nc), 7, 7)
#'
#' biological.effect.train.ind <- colnames(biological.effect.nc)[c(sample(which(
#'   group.id == "E"), size = 64),
#'   sample(which(group.id == "V"), size = 64))]
#' biological.effect.nc.tr <- biological.effect.nc[, biological.effect.train.ind]
#'
#' knn.int <- knn.intcv(X = biological.effect.nc.tr,
#'                      y = substr(colnames(biological.effect.nc.tr), 7, 7),
#'                      kfold = 5, seed = 1)
#'



"knn.intcv" <- function(kfold = 5, X, y, seed){
  ptm <- proc.time()
  set.seed(seed)


  ctrl <- trainControl(method = "repeatedcv",
                       repeats = 3,
                       number = kfold)
  knn <- train(x = data.matrix(t(X)), y = factor(y),
               method = "knn",
               tuneLength = 9,
               trControl = ctrl)

  time <- proc.time() - ptm
  return(list(mc = 1 - max(knn$results$Accuracy), time = time, model = knn, cfs = NULL))
}