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coxDKsplsDR.R
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coxDKsplsDR.R
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#' Fitting a Direct Kernel sPLSR model on the (Deviance) Residuals
#'
#' This function computes the Cox Model based on sPLSR components computed
#' model with \itemize{\item as the response: the Residuals of a Cox-Model
#' fitted with no covariate \item as explanatory variables: a Kernel
#' transform of Xplan.}
#' It uses the package \code{kernlab} to compute the Kernel
#' transforms of Xplan, the package \code{spls} to perform the first step in
#' SPLSR then \code{mixOmics} to perform PLSR step fit.
#'
#' If \code{allres=FALSE} returns only the final Cox-model. If
#' \code{allres=TRUE} returns a list with the sPLS components, the final
#' Cox-model and the sPLSR model. \code{allres=TRUE} is useful for evluating
#' model prediction accuracy on a test sample.
#'
#' @aliases coxDKsplsDR coxDKsplsDR.default coxDKsplsDR.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 ncomp The number of components to include in the model. The number of
#' components to fit is specified with the argument ncomp. It this is not
#' supplied, the maximal number of components is used.
#' @param modepls character string. What type of algorithm to use, (partially)
#' matching one of "regression", "canonical", "invariant" or "classic". See
#' \code{\link[mixOmics]{pls}} for details
#' @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{coxDKsplsDR} 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 eta Thresholding parameter. \code{eta} should be between 0 and 1.
#' @param trace Print out the progress of variable selection?
#' @param kernel the kernel function used in training and predicting. This
#' parameter can be set to any function, of class kernel, which computes the
#' inner product in feature space between two vector arguments (see
#' \link[kernlab]{kernels}). The \code{kernlab} package provides the most
#' popular kernel functions which can be used by setting the kernel parameter
#' to the following strings: \describe{ \item{list("rbfdot")}{Radial Basis
#' kernel "Gaussian"} \item{list("polydot")}{Polynomial kernel}
#' \item{list("vanilladot")}{Linear kernel} \item{list("tanhdot")}{Hyperbolic
#' tangent kernel} \item{list("laplacedot")}{Laplacian kernel}
#' \item{list("besseldot")}{Bessel kernel} \item{list("anovadot")}{ANOVA RBF
#' kernel} \item{list("splinedot")}{Spline kernel} }
#' @param hyperkernel the list of hyper-parameters (kernel parameters). This is
#' a list which contains the parameters to be used with the kernel function.
#' For valid parameters for existing kernels are : \itemize{ \item
#' \code{sigma}, inverse kernel width for the Radial Basis kernel function
#' "rbfdot" and the Laplacian kernel "laplacedot". \item \code{degree},
#' \code{scale}, \code{offset} for the Polynomial kernel "polydot". \item
#' \code{scale}, offset for the Hyperbolic tangent kernel function "tanhdot".
#' \item \code{sigma}, \code{order}, \code{degree} for the Bessel kernel
#' "besseldot". \item \code{sigma}, \code{degree} for the ANOVA kernel
#' "anovadot". } In the case of a Radial Basis kernel function (Gaussian) or
#' Laplacian kernel, if \code{hyperkernel} is missing, the heuristics in sigest
#' are used to calculate a good sigma value from the data.
#' @param verbose Should some details be displayed ?
#' @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}.
#' @return If \code{allres=FALSE} : \item{cox_DKsplsDR}{Final Cox-model.} If
#' \code{allres=TRUE} : \item{tt_DKsplsDR}{sPLSR components.}
#' \item{cox_DKsplsDR}{Final Cox-model.} \item{DKsplsDR_mod}{The sPLSR 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[pls]{plsr}}
#' @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_DKsplsDR_fit=coxDKsplsDR(X_train_micro,Y_train_micro,C_train_micro,ncomp=6,
#' validation="CV",eta=.5))
#' (cox_DKsplsDR_fit=coxDKsplsDR(~X_train_micro,Y_train_micro,C_train_micro,ncomp=6,
#' validation="CV",eta=.5))
#' (cox_DKsplsDR_fit=coxDKsplsDR(~.,Y_train_micro,C_train_micro,ncomp=6,
#' validation="CV",dataXplan=data.frame(X_train_micro),eta=.5))
#'
#' (cox_DKsplsDR_fit=coxDKsplsDR(X_train_micro,Y_train_micro,C_train_micro,ncomp=6,
#' validation="CV",allres=TRUE,eta=.5))
#' (cox_DKsplsDR_fit=coxDKsplsDR(~X_train_micro,Y_train_micro,C_train_micro,ncomp=6,
#' validation="CV",allres=TRUE,eta=.5))
#' (cox_DKsplsDR_fit=coxDKsplsDR(~.,Y_train_micro,C_train_micro,ncomp=6,
#' validation="CV",allres=TRUE,dataXplan=data.frame(X_train_micro),eta=.5))
#'
#' rm(X_train_micro,Y_train_micro,C_train_micro,cox_DKsplsDR_fit)
#'
#' @export coxDKsplsDR
coxDKsplsDR <- function (Xplan, ...) UseMethod("coxDKsplsDR")