Nothing
#' Partial least squares Regression generalized linear models
#'
#' This function implements an extension of Partial least squares Regression to
#' Cox Models.
#'
#'
#' A typical predictor has the form response ~ terms where response is the
#' (numeric) response vector and terms is a series of terms which specifies a
#' linear predictor for response. A terms specification of the form first +
#' second indicates all the terms in first together with all the terms in
#' second with any duplicates removed.
#'
#' A specification of the form first:second indicates the the set of terms
#' obtained by taking the interactions of all terms in first with all terms in
#' second. The specification first*second indicates the cross of first and
#' second. This is the same as first + second + first:second.
#'
#' The terms in the formula will be re-ordered so that main effects come first,
#' followed by the interactions, all second-order, all third-order and so on:
#' to avoid this pass a terms object as the formula.
#'
#' Non-NULL weights can be used to indicate that different observations have
#' different dispersions (with the values in weights being inversely
#' proportional to the dispersions); or equivalently, when the elements of
#' weights are positive integers w_i, that each response y_i is the mean of w_i
#' unit-weight observations.
#'
#' @aliases plsRcox plsRcoxmodel.default plsRcoxmodel.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 components to be extracted
#' @param limQ2set limit value for the Q2
#' @param dataPredictY predictor(s) (testing) dataset
#' @param pvals.expli should individual p-values be reported to tune model
#' selection ?
#' @param alpha.pvals.expli level of significance for predictors when
#' pvals.expli=TRUE
#' @param tol_Xi minimal value for Norm2(Xi) and \eqn{\mathrm{det}(pp' \times
#' pp)}{det(pp'*pp)} if there is any missing value in the \code{dataX}. It
#' defaults to \eqn{10^{-12}}{10^{-12}}
#' @param weights an optional vector of 'prior weights' to be used in the
#' fitting process. Should be \code{NULL} or a numeric vector.
#' @param subset an optional vector specifying a subset of observations to be
#' used in the fitting process.
#' @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{coxDKplsDR} is called.
#' @param model_frame If \code{TRUE}, the model frame is returned.
#' @param method the method to be used in fitting the model. The default method
#' \code{"glm.fit"} uses iteratively reweighted least squares (IWLS).
#' User-supplied fitting functions can be supplied either as a function or a
#' character string naming a function, with a function which takes the same
#' arguments as \code{glm.fit}.
#' @param control a list of parameters for controlling the fitting process. For
#' \code{glm.fit} this is passed to \code{\link{glm.control}}.
#' @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 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 pass to \code{plsRmodel.default} or to
#' \code{plsRmodel.formula}
#' @return Depends on the model that was used to fit the model.
#' @author Frédéric Bertrand\cr
#' \email{frederic.bertrand@@utt.fr}\cr
#' \url{http://www-irma.u-strasbg.fr/~fbertran/}
#' @seealso \code{\link[plsRglm]{plsR}} and \code{\link[plsRglm]{plsRglm}}
#' @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]
#'
#' plsRcox(X_train_micro,time=Y_train_micro,event=C_train_micro,nt=5)
#' plsRcox(~X_train_micro,time=Y_train_micro,event=C_train_micro,nt=5)
#'
#' plsRcox(Xplan=X_train_micro,time=Y_train_micro,event=C_train_micro,nt=5,sparse=TRUE,
#' alpha.pvals.expli=.15)
#' plsRcox(Xplan=~X_train_micro,time=Y_train_micro,event=C_train_micro,nt=5,sparse=TRUE,
#' alpha.pvals.expli=.15)
#'
#' @export plsRcox
plsRcox <- function (Xplan, ...) UseMethod("plsRcoxmodel")
#' @rdname plsRcox
#' @aliases plsRcox
#' @export plsRcoxmodel
plsRcoxmodel <- plsRcox
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.