R/cv.clogitLasso.R

In clogitLasso: Sparse Conditional Logistic Regression for Matched Studies

#' Cross-validation of \code{clogitLasso} object
#'
#' Cross-validation of \code{clogitLasso} object
#'
#' @param objclogitLasso An objet of type \code{clogitLasso}
#' @param K The number of folds used in cross validation
#' @param gpe A list of group defined by the user.
#' @return An object of type \code{cv.clogitLasso} with the following components:
#'
#' \item{lambda}{Vector of regularisation parameter}
#'
#' \item{mean_cv}{vector of mean deviances for each value of the regularisation parameter}
#'
#' \item{beta}{Vector of estimated coefficients with optimal regularisation parameter}
#'
#' \item{lambdaopt}{Optimal regularisation parameter}
#' @author Marta Avalos, Helene Pouyes, Marius Kwemou and Binbin Xu
#' @references Avalos, M., Pouyes, H., Grandvalet, Y., Orriols, L., & Lagarde, E. (2015). \emph{Sparse conditional logistic
#' regression for analyzing large-scale matched data from epidemiological studies: a simple algorithm.} BMC bioinformatics, 16(6), S1.  \doi{10.1186/1471-2105-16-S6-S1}.
#' @examples
#' \dontrun{
#' # generate data
#' y <- rep(c(1,0), 100)
#' X <- matrix (rnorm(20000, 0, 1), ncol = 100) # pure noise
#' strata <- sort(rep(1:100, 2))
#'
#' # fitLasso <- clogitLasso(X,y,strata,log=TRUE)
#' 
#' # Cross validation
#' cv.fit <- cv.clogitLasso(fitLasso)
#' }
#' @export

cv.clogitLasso <- function(objclogitLasso,
                          K = 10,
                          gpe = NULL) {
  if (table(objclogitLasso$arg$strata)[1] == 2) {
    res <- crossvalidation(objclogitLasso, K, gpe = NULL)
  } else{
    res <- crossvalidation1M(objclogitLasso, K, gpe = NULL)
  }
  class(res) <- "cv.clogitLasso"
  return(res)
}