R/xgboost.intcv.R

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

#' XGBoost  Classifier
#'
#' Build a random forest classifier using internal cross validation to choose the turning parameter, with a 5-fold cross validation as default.
#'
#' @references https://cran.r-project.org/web/packages/e1071/index.html
#' @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 LASSO}
#' \item{model}{a LASSO classifier, resulted from \code{cv.fit}}
#' @export
#' @import xgboost
#' @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]
#'
#' ranfor.int <- ranfor.intcv(X = biological.effect.nc.tr,
#'                          y = substr(colnames(biological.effect.nc.tr), 7, 7),
#'                          kfold = 5, seed = 1)
#'


"xgboost.intcv" <- function(kfold = 5, X, y, seed){
  ptm <- proc.time()
  set.seed(seed)
  
  
  control <- trainControl(method='cv', 
                          number=kfold, 
                          search = 'random')
  
  rf <- train(x = data.matrix(t(X)), y = factor(y),
              method = 'rf',
              metric = 'Accuracy',
              tuneLength = 10,
              trControl = control)
  
  time <- proc.time() - ptm
  return(list(mc = 1 - max(rf$results$Accuracy), time = time, model = rf, cfs = NULL))
}