R/coxpls3DR.R

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

#' Fitting a PLSR model on the (Deviance) Residuals
#' 
#' This function computes the PLSR model with the Residuals of a Cox-Model
#' fitted with an intercept as the only explanatory variable as the response
#' and Xplan as explanatory variables. Default behaviour uses the Deviance
#' residuals. It uses the package \code{plsRglm}.
#' 
#' If \code{allres=FALSE} returns only the final Cox-model. If
#' \code{allres=TRUE} returns a list with the PLS components, the final
#' Cox-model and the PLSR model. \code{allres=TRUE} is useful for evluating
#' model prediction accuracy on a test sample.
#' 
#' @aliases coxpls3DR coxpls3DR.default coxpls3DR.formula
#' @param Xplan a formula or a matrix with the eXplanatory variables (training)
#' dataset
#' @param time for right censored data, this is the follow up time. For
#' interval data, the first argument is the starting time for the interval.
#' @param time2 The status indicator, normally 0=alive, 1=dead. Other choices
#' are \code{TRUE/FALSE} (\code{TRUE} = death) or 1/2 (2=death). For interval
#' censored data, the status indicator is 0=right censored, 1=event at
#' \code{time}, 2=left censored, 3=interval censored. Although unusual, the
#' event indicator can be omitted, in which case all subjects are assumed to
#' have an event.
#' @param event ending time of the interval for interval censored or counting
#' process data only. Intervals are assumed to be open on the left and closed
#' on the right, \code{(start, end]}. For counting process data, event
#' indicates whether an event occurred at the end of the interval.
#' @param type character string specifying the type of censoring. Possible
#' values are \code{"right"}, \code{"left"}, \code{"counting"},
#' \code{"interval"}, or \code{"interval2"}. The default is \code{"right"} or
#' \code{"counting"} depending on whether the \code{time2} argument is absent
#' or present, respectively.
#' @param origin for counting process data, the hazard function origin. This
#' option was intended to be used in conjunction with a model containing time
#' dependent strata in order to align the subjects properly when they cross
#' over from one strata to another, but it has rarely proven useful.
#' @param typeres character string indicating the type of residual desired.
#' Possible values are \code{"martingale"}, \code{"deviance"}, \code{"score"},
#' \code{"schoenfeld"}, \code{"dfbeta"}, \code{"dfbetas"}, and
#' \code{"scaledsch"}. Only enough of the string to determine a unique match is
#' required.
#' @param collapse vector indicating which rows to collapse (sum) over. In
#' time-dependent models more than one row data can pertain to a single
#' individual. If there were 4 individuals represented by 3, 1, 2 and 4 rows of
#' data respectively, then \code{collapse=c(1,1,1,2,3,3,4,4,4,4)} could be used
#' to obtain per subject rather than per observation residuals.
#' @param weighted if \code{TRUE} and the model was fit with case weights, then
#' the weighted residuals are returned.
#' @param scaleX Should the \code{Xplan} columns be standardized ?
#' @param scaleY Should the \code{time} values be standardized ?
#' @param nt Number of PLSR components to fit.
#' @param typeVC type of leave one out crossed validation. Several procedures
#' are available and may be forced.  \describe{ \item{list("none")}{no crossed
#' validation} \item{list("standard")}{as in SIMCA for datasets without missing
#' values and with all values predicted as those with missing values for
#' datasets with any missing values} \item{list("missingdata")}{all values
#' predicted as those with missing values for datasets with any missing values}
#' \item{list("adaptative")}{predict a response value for an x with any missing
#' value as those with missing values and for an x without any missing value as
#' those without missing values.} }
#' @param plot Should the survival function be plotted ?)
#' @param allres FALSE to return only the Cox model and TRUE for additionnal
#' results. See details. Defaults to FALSE.
#' @param dataXplan an optional data frame, list or environment (or object
#' coercible by \code{\link{as.data.frame}} to a data frame) containing the
#' variables in the model. If not found in \code{dataXplan}, the variables are
#' taken from \code{environment(Xplan)}, typically the environment from which
#' \code{coxpls3DR} is called.
#' @param subset an optional vector specifying a subset of observations to be
#' used in the fitting process.
#' @param weights an optional vector of 'prior weights' to be used in the
#' fitting process. Should be \code{NULL} or a numeric vector.
#' @param model_frame If \code{TRUE}, the model frame is returned.
#' @param sparse should the coefficients of non-significant predictors
#' (<\code{alpha.pvals.expli}) be set to 0
#' @param sparseStop should component extraction stop when no significant
#' predictors (<\code{alpha.pvals.expli}) are found
#' @param model_matrix If \code{TRUE}, the model matrix is returned.
#' @param contrasts.arg a list, whose entries are values (numeric matrices, 
#' functions or character strings naming functions) to be used as replacement 
#' values for the contrasts replacement function and whose names are the names 
#' of columns of data containing factors.
#' @param \dots Arguments to be passed on to \code{survival::coxph} and to
#' \code{plsRglm::PLS_lm}.
#' @return If \code{allres=FALSE} : \item{cox_pls3DR}{Final Cox-model.} If
#' \code{allres=TRUE} : \item{tt_pls3DR}{PLSR components.}
#' \item{cox_pls3DR}{Final Cox-model.} \item{pls3DR_mod}{The PLSR model.}
#' @author Frédéric Bertrand\cr
#' \email{frederic.bertrand@@utt.fr}\cr
#' \url{http://www-irma.u-strasbg.fr/~fbertran/}
#' @seealso \code{\link[survival]{coxph}}, \code{\link[plsRglm]{PLS_lm}}
#' @references plsRcox, Cox-Models in a high dimensional setting in R, Frederic
#' Bertrand, Philippe Bastien, Nicolas Meyer and Myriam Maumy-Bertrand (2014).
#' Proceedings of User2014!, Los Angeles, page 152.\cr
#' 
#' Deviance residuals-based sparse PLS and sparse kernel PLS regression for
#' censored data, Philippe Bastien, Frederic Bertrand, Nicolas Meyer and Myriam
#' Maumy-Bertrand (2015), Bioinformatics, 31(3):397-404,
#' doi:10.1093/bioinformatics/btu660.
#' @keywords models regression
#' @examples
#' 
#' data(micro.censure)
#' 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)
#' Y_train_micro <- micro.censure$survyear[1:80]
#' C_train_micro <- micro.censure$DC[1:80]
#' 
#' (cox_pls3DR_fit <- coxpls3DR(X_train_micro,Y_train_micro,C_train_micro,nt=7))
#' (cox_pls3DR_fit2 <- coxpls3DR(~X_train_micro,Y_train_micro,C_train_micro,nt=7))
#' (cox_pls3DR_fit3 <- coxpls3DR(~.,Y_train_micro,C_train_micro,nt=7,dataXplan=X_train_micro_df))
#' (cox_pls3DR_fit4 <- coxpls3DR(~.,Y_train_micro,C_train_micro,nt=7,typeVC="none",
#' data=X_train_micro_df,sparse=TRUE))
#' (cox_pls3DR_fit5 <- coxpls3DR(~.,Y_train_micro,C_train_micro,nt=7,typeVC="none",
#' data=X_train_micro_df,sparse=TRUE,sparseStop=FALSE))
#' 
#' rm(X_train_micro,Y_train_micro,C_train_micro,cox_pls3DR_fit,cox_pls3DR_fit2,
#' cox_pls3DR_fit3,cox_pls3DR_fit4,cox_pls3DR_fit5)
#' 
#' 
#' @export coxpls3DR
coxpls3DR <- function (Xplan, ...) UseMethod("coxpls3DR")