R/plsRcox-package.R

In plsRcox: Partial Least Squares Regression for Cox Models and Related Techniques

#' @title plsRcox-package: Partial Least Squares Regression for Cox Models and Related Techniques
#'
#' @description Provides Partial least squares Regression and various regular, sparse or kernel, techniques for fitting Cox models in high dimensional settings <doi:10.1093/bioinformatics/btu660>, Bastien, P., Bertrand, F., Meyer N., Maumy-Bertrand, M. (2015), Deviance residuals-based sparse PLS and sparse kernel PLS regression for censored data, Bioinformatics, 31(3):397-404. Cross validation criteria were studied in <arXiv:1810.02962>, Bertrand, F., Bastien, Ph. and Maumy-Bertrand, M. (2018), Cross validating extensions of kernel, sparse or regular partial least squares regression models to censored data.
#'
#' @docType package
#' @name plsRcox-package
#' @references Bastien, P., Bertrand, F., Meyer N., Maumy-Bertrand, M. (2015), Deviance residuals-based sparse PLS and sparse kernel PLS regression for censored data, Bioinformatics, 31(3):397-404. <doi:10.1093/bioinformatics/btu660>. Cross validation criteria were studied in <arXiv:1810.02962>, Bertrand, F., Bastien, Ph. and Maumy-Bertrand, M. (2018), Cross validating extensions of kernel, sparse or regular partial least squares regression models to censored data.
#' 
#' @import survival
#' @import kernlab
#' @import lars
#' @import pls
#' @import plsRglm
#' @import mixOmics
#' @import risksetROC
#' @import survcomp
#' @import survAUC
#' @import rms
#' @importFrom grDevices dev.new
#' @importFrom graphics abline axis layout segments
#' @importFrom stats as.formula complete.cases contrasts 
#' extractAIC is.empty.model median model.matrix 
#' model.response model.weights residuals uniroot 
#' var weighted.mean
#' @importFrom utils head
#' 
#' @examples
#' # The original allelotyping dataset
#' 
#' library(plsRcox)
#' data(micro.censure)
#' Y_train_micro <- micro.censure$survyear[1:80]
#' C_train_micro <- micro.censure$DC[1:80]
#' Y_test_micro <- micro.censure$survyear[81:117]
#' C_test_micro <- micro.censure$DC[81:117]
#' 
#' data(Xmicro.censure_compl_imp)
#' X_train_micro <- apply((as.matrix(Xmicro.censure_compl_imp)),
#' FUN="as.numeric",MARGIN=2)[1:80,]
#' X_train_micro_df <- data.frame(X_train_micro)
#' 
#' # coxsplsDR
#' cox_splsDR_fit=coxsplsDR(X_train_micro,Y_train_micro,C_train_micro,
#' ncomp=6,eta=.5)
#' cox_splsDR_fit
#' cox_splsDR_fit2=coxsplsDR(~X_train_micro,Y_train_micro,C_train_micro,
#' ncomp=6,eta=.5,trace=TRUE)
#' cox_splsDR_fit2
#' cox_splsDR_fit3=coxsplsDR(~.,Y_train_micro,C_train_micro,ncomp=6,
#' dataXplan=X_train_micro_df,eta=.5)
#' cox_splsDR_fit3
#' rm(cox_splsDR_fit,cox_splsDR_fit2,cox_splsDR_fit3)
#' 
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