R/calcSeCSC.R
In bozenne/riskRegressionLight: Light version of the package riskRegression
### calcSeCSC.R ---
#----------------------------------------------------------------------
## author: Brice Ozenne
## created: maj 27 2017 (21:23)
## Version:
## last-updated: Jan 29 2019 (16:02)
## By: Thomas Alexander Gerds
## Update #: 502
#----------------------------------------------------------------------
##
### Commentary:
##
### Change Log:
#----------------------------------------------------------------------
##
### Code:
## * calcSeCSC (documentation)
#' @title Standard error of the absolute risk predicted from cause-specific Cox models
#' @description Standard error of the absolute risk predicted from cause-specific Cox models
#' using a first order von Mises expansion of the absolute risk functional.
#' @name calcSeCSC
#'
#' @param object The fitted cause specific Cox model
#' @param cif the cumulative incidence function at each prediction time for each individual.
#' @param hazard list containing the baseline hazard for each cause in a matrix form. Columns correspond to the strata.
#' @param cumhazard list containing the cumulative baseline hazard for each cause in a matrix form. Columns correspond to the strata.
#' @param object.time a vector containing all the events regardless to the cause.
#' @param object.maxtime a matrix containing the latest event in the strata of the observation for each cause.
#' @param eXb a matrix containing the exponential of the linear predictor evaluated for the new observations (rows) for each cause (columns)
#' @param new.LPdata a list of design matrices for the new observations for each cause.
#' @param new.strata a matrix containing the strata indicator for each observation and each cause.
#' @param times the time points at which to evaluate the predictions.
#' @param surv.type see the surv.type argument of \code{\link{CSC}}.
#' @param ls.infoVar A list containing the output of \code{coxVariableName} for each Cox model.
#' @param new.n the number of new observations.
#' @param cause the cause of interest.
#' @param nCause the number of causes.
#' @param nVar the number of variables that form the linear predictor in each Cox model
#' @param export can be "iid" to return the value of the influence function for each observation
#' "se" to return the standard error for a given timepoint
#'
#' @param store.iid the method used to compute the influence function and the standard error.
#' Can be \code{"full"} or \code{"minimal"}. See the details section.
#'
#' @details Can also return the empirical influence function of the functionals cumulative hazard or survival
#' or the sum over the observations of the empirical influence function.
#'
#' \code{store.iid="full"} compute the influence function for each observation at each time in the argument \code{times}
#' before computing the standard error / influence functions.
#' \code{store.iid="minimal"} recompute for each subject specific prediction the influence function for the baseline hazard.
#' This avoid to store all the influence functions but may lead to repeated evaluation of the influence function.
#' This solution is therefore efficient more efficient in memory usage but may not be in term of computation time.
## * calcSeCSC (code)
#' @rdname calcSeCSC
calcSeCSC <- function(object, cif, hazard, cumhazard, object.time, object.maxtime,
eXb, new.LPdata, new.strata, times, surv.type, ls.infoVar,
new.n, cause, nCause, nVar, export, store.iid){
status <- strata.num <- NULL ## [:CRANcheck:] data.table
# {{{ influence function for each Cox model
if(is.null(object$iid)){
object$iid <- list()
for(iModel in 1:nCause){ # iModel <- 1
object$iid[[iModel]] <- iidCox(object$models[[iModel]], tau.hazard = object.time, store.iid = store.iid)
}
}else{
store.iid <- object$iid[[1]]$store.iid
for(iModel in 1:nCause){
object$iid[[iModel]] <- selectJump(object$iid[[iModel]], times = object.time,
type = c("hazard","cumhazard"))
}
}
# }}}
# {{{ prepare arguments
nEtimes <- length(object.time)
object.n <- NROW(object$iid[[1]]$IFbeta)
if(store.iid == "minimal"){
## {{{ method = "minimal"
out <- list()
if("se" %in% export){
out$se <- matrix(NA, nrow = new.n, ncol = length(times))
}
if("iid" %in% export){
out$iid <- array(NA, dim = c(new.n, length(times), object.n))
}
if("average.iid" %in% export){
out$average.iid <- matrix(0, nrow = object.n, ncol = length(times))
}
object.modelFrame <- coxModelFrame(object$models[[cause]])
object.modelFrame[, c("strata.num") := as.numeric(.SD$strata)-1]
## recover strata in the training dataset
M.object.strata.num <- do.call(cbind,lapply(1:nCause, function(iCause){
as.numeric(coxStrata(object$models[[iCause]], data = NULL,
strata.vars = ls.infoVar[[iCause]]$stratavars))-1
}))
object.Ustrata <- do.call(paste0, as.data.frame(M.object.strata.num))
## collapse strata variables into on variable
level.strata <- unique(new.strata)
new.Ustrata <- do.call(paste0, as.data.frame(new.strata))
level.Ustrata <- unique(new.Ustrata)
nStrata <- length(level.Ustrata)
for(iStrata in 1:nStrata){ # iStrata <- 1
# {{{ prepare arguments
nTime <- length(times)
iStrataTheCause <- new.strata[iStrata,cause]
indexStrataTheCause <- which(new.Ustrata==level.Ustrata[iStrata])
ls.args <- list()
ls.args$seqTau <- times
ls.args$jumpTime <- object.time
ls.args$jumpTheCause <- object.time %in% object.modelFrame[status==1&strata.num==iStrataTheCause,stop]
ls.args$IFbeta <- lapply(object$iid, function(x){x$IFbeta})
ls.args$cif <- cif[indexStrataTheCause,,drop=FALSE]
ls.args$iS0 <- do.call(cbind,lapply(object$iid, function(x){
x$calcIFhazard$delta_iS0[[iStrataTheCause+1]]
}))
ls.args$newEXb <- eXb[indexStrataTheCause,,drop=FALSE]
ls.args$sampleEXb <- exp(do.call(cbind,lapply(object$models, coxLP, data = NULL, center = FALSE)))
ls.args$sampleTime <- object.modelFrame[["stop"]]
ls.args$indexJump <- matrix(NA, ncol = nCause, nrow = nEtimes)
ls.args$indexSample <- matrix(NA, ncol = nCause, nrow = object.n)
ls.args$Ehazard0 <- vector(mode = "list", length = nCause)
ls.args$cumEhazard0 <- vector(mode = "list", length = nCause)
ls.args$cumhazard_iS0 <- vector(mode = "list", length = nCause)
ls.args$hazard_iS0 <- vector(mode = "list", length = nCause)
ls.args$X <- vector(mode = "list", length = nCause)
ls.args$sameStrata <- matrix(NA,nrow = object.n, ncol = nCause)
ls.args$cumhazard0 <- vector(mode = "list", length = nCause)
ls.args$hazard0 <- vector(mode = "list", length = nCause)
for(iterC in 1:nCause){ # iterC <- 1
iStrataCause <- level.strata[iStrata,iterC]
ls.args$indexJump[,iterC] <- prodlim::sindex(object$iid[[iterC]]$calcIFhazard$time1[[iStrataCause + 1]],
eval.times = object.time)
ls.args$indexSample[,iterC] <- prodlim::sindex(object$iid[[iterC]]$calcIFhazard$time1[[iStrataCause + 1]],
eval.times = object.modelFrame[["stop"]])
ls.args$cumEhazard0[[iterC]] <- object$iid[[iterC]]$calcIFhazard$cumElambda0[[iStrataCause + 1]]
ls.args$Ehazard0[[iterC]] <- object$iid[[iterC]]$calcIFhazard$Elambda0[[iStrataCause + 1]]
ls.args$cumhazard_iS0[[iterC]] <- c(0,object$iid[[iterC]]$calcIFhazard$cumLambda0_iS0[[iStrataCause + 1]])## add 0 to match prodlim
ls.args$hazard_iS0[[iterC]] <- c(0,object$iid[[iterC]]$calcIFhazard$lambda0_iS0[[iStrataCause + 1]]) ## add 0 to match prodlim
ls.args$X[[iterC]] <- new.LPdata[[iterC]][indexStrataTheCause,,drop=FALSE]
ls.args$sameStrata[,iterC] <- M.object.strata.num[,iterC]==iStrataCause
ls.args$hazard0[[iterC]] <- hazard[[iterC]][,iStrataCause + 1]
ls.args$cumhazard0[[iterC]] <- cumhazard[[iterC]][,iStrataCause + 1]
}
# }}}
ls.args$theCause <- cause-1
ls.args$firstJumpTime <- object$iid[[cause]]$etime1.min[iStrataTheCause+1]
ls.args$lastSampleTime <- object.modelFrame[strata.num==iStrataCause,max(.SD$stop)]
ls.args$nTau <- nTime
ls.args$nJump <- nEtimes
ls.args$nNewObs <- length(indexStrataTheCause)
ls.args$nSample <- object.n
ls.args$nCause <- nCause
ls.args$p <- nVar
ls.args$survtype <- surv.type=="survival"
ls.args$exportSE<- ("se" %in% export)
ls.args$exportIF <- ("iid" %in% export)
ls.args$exportIFsum <- ("average.iid" %in% export)
resCpp <- do.call(calcSeCif_cpp, args = ls.args)
if("se" %in% export){
out$se[indexStrataTheCause,] <- resCpp$se
}
if("iid" %in% export){
out$iid[indexStrataTheCause,,] <- resCpp$iid
}
if("average.iid" %in% export){
out$average.iid <- out$average.iid + resCpp$iidsum/new.n
}
}
# }}}
}else{
# {{{ other method
nTimes <- length(times)
sindex.times <- prodlim::sindex(object.time, eval.times = times)-1 ## i.e. -1 is before the first jump
for(iCause in 1:nCause){ ## remove attributes to have a list of matrices
attr(new.LPdata[[iCause]],"assign") <- NULL
attr(new.LPdata[[iCause]],"contrasts") <- NULL
}
if("iid" %in% export || "se" %in% export){
out <- calcSeCif2_cpp(ls_IFbeta = lapply(object$iid,"[[","IFbeta"),
ls_X = new.LPdata,
ls_cumhazard = cumhazard,
ls_hazard = hazard[[cause]],
ls_IFcumhazard = lapply(object$iid,"[[","IFcumhazard"),
ls_IFhazard = object$iid[[cause]]$IFhazard,
eXb = eXb,
nJumpTime = nEtimes, JumpMax = object.maxtime,
tau = times, tauIndex = sindex.times, nTau = nTimes,
nObs = object.n,
theCause = (cause-1), nCause = nCause, hazardType = (surv.type=="hazard"), nVar = nVar,
nNewObs = new.n, strata = new.strata,
exportSE = "se" %in% export, exportIF = "iid" %in% export, exportIFsum = "average.iid" %in% export)
if("iid" %in% export){
out$iid <- aperm(out$iid, c(1,3,2))
}
} else if("average.iid" %in% export){
new.level.strata <- unique(new.strata)
new.level.Ustrata <- apply(new.level.strata,1,paste0,collapse="")
new.n.strata <- length(new.level.Ustrata)
new.Ustrata <- apply(new.strata,1,paste0,collapse="")
new.indexStrata <- lapply(new.level.Ustrata, function(iStrata){
which(new.Ustrata==iStrata) - 1
})
if(is.null(attr(export,"factor"))){
rm.list <- TRUE
factor <- matrix(1, nrow = new.n, ncol = 1)
}else{
rm.list <- FALSE
factor <- attr(export, "factor")
}
outRcpp <- calcAIFcif_cpp(hazard1 = hazard[[cause]],
ls_cumhazard = cumhazard,
ls_tX = lapply(new.LPdata,t),
eXb = eXb,
ls_IFbeta = lapply(object$iid,"[[","IFbeta"),
ls_IFhazard = object$iid[[cause]]$IFhazard,
ls_IFcumhazard = lapply(object$iid,"[[","IFcumhazard"),
nCause = nCause, theCause = cause-1, hazardType = (surv.type == "hazard"),
nJumpTime = nEtimes, JumpMax = tapply(object.maxtime,new.Ustrata,max),
tau = times, tauIndex = sindex.times, nTau = nTimes,
nObs = object.n, nNewObs = new.n,
levelStrata = new.level.strata, nStrata = new.n.strata, ls_indexStrata = new.indexStrata,
nVar = nVar,
factor = factor)
if(rm.list){
out <- list(average.iid = matrix(outRcpp[[1]], nrow = object.n, ncol = nTimes))
}else{
out <- list(average.iid = lapply(outRcpp, function(iMat){matrix(iMat, nrow = object.n, ncol = nTimes)}))
}
}
# }}}
}
return(out)
}
#----------------------------------------------------------------------
### calcSeCSC.R ends here
bozenne/riskRegressionLight documentation built on May 7, 2019, 12:52 a.m.
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### calcSeCSC.R ---
#----------------------------------------------------------------------
## author: Brice Ozenne
## created: maj 27 2017 (21:23)
## Version:
## last-updated: Jan 29 2019 (16:02)
## By: Thomas Alexander Gerds
## Update #: 502
#----------------------------------------------------------------------
##
### Commentary:
##
### Change Log:
#----------------------------------------------------------------------
##
### Code:
## * calcSeCSC (documentation)
#' @title Standard error of the absolute risk predicted from cause-specific Cox models
#' @description Standard error of the absolute risk predicted from cause-specific Cox models
#' using a first order von Mises expansion of the absolute risk functional.
#' @name calcSeCSC
#'
#' @param object The fitted cause specific Cox model
#' @param cif the cumulative incidence function at each prediction time for each individual.
#' @param hazard list containing the baseline hazard for each cause in a matrix form. Columns correspond to the strata.
#' @param cumhazard list containing the cumulative baseline hazard for each cause in a matrix form. Columns correspond to the strata.
#' @param object.time a vector containing all the events regardless to the cause.
#' @param object.maxtime a matrix containing the latest event in the strata of the observation for each cause.
#' @param eXb a matrix containing the exponential of the linear predictor evaluated for the new observations (rows) for each cause (columns)
#' @param new.LPdata a list of design matrices for the new observations for each cause.
#' @param new.strata a matrix containing the strata indicator for each observation and each cause.
#' @param times the time points at which to evaluate the predictions.
#' @param surv.type see the surv.type argument of \code{\link{CSC}}.
#' @param ls.infoVar A list containing the output of \code{coxVariableName} for each Cox model.
#' @param new.n the number of new observations.
#' @param cause the cause of interest.
#' @param nCause the number of causes.
#' @param nVar the number of variables that form the linear predictor in each Cox model
#' @param export can be "iid" to return the value of the influence function for each observation
#' "se" to return the standard error for a given timepoint
#'
#' @param store.iid the method used to compute the influence function and the standard error.
#' Can be \code{"full"} or \code{"minimal"}. See the details section.
#'
#' @details Can also return the empirical influence function of the functionals cumulative hazard or survival
#' or the sum over the observations of the empirical influence function.
#'
#' \code{store.iid="full"} compute the influence function for each observation at each time in the argument \code{times}
#' before computing the standard error / influence functions.
#' \code{store.iid="minimal"} recompute for each subject specific prediction the influence function for the baseline hazard.
#' This avoid to store all the influence functions but may lead to repeated evaluation of the influence function.
#' This solution is therefore efficient more efficient in memory usage but may not be in term of computation time.
## * calcSeCSC (code)
#' @rdname calcSeCSC
calcSeCSC <- function(object, cif, hazard, cumhazard, object.time, object.maxtime,
eXb, new.LPdata, new.strata, times, surv.type, ls.infoVar,
new.n, cause, nCause, nVar, export, store.iid){
status <- strata.num <- NULL ## [:CRANcheck:] data.table
# {{{ influence function for each Cox model
if(is.null(object$iid)){
object$iid <- list()
for(iModel in 1:nCause){ # iModel <- 1
object$iid[[iModel]] <- iidCox(object$models[[iModel]], tau.hazard = object.time, store.iid = store.iid)
}
}else{
store.iid <- object$iid[[1]]$store.iid
for(iModel in 1:nCause){
object$iid[[iModel]] <- selectJump(object$iid[[iModel]], times = object.time,
type = c("hazard","cumhazard"))
}
}
# }}}
# {{{ prepare arguments
nEtimes <- length(object.time)
object.n <- NROW(object$iid[[1]]$IFbeta)
if(store.iid == "minimal"){
## {{{ method = "minimal"
out <- list()
if("se" %in% export){
out$se <- matrix(NA, nrow = new.n, ncol = length(times))
}
if("iid" %in% export){
out$iid <- array(NA, dim = c(new.n, length(times), object.n))
}
if("average.iid" %in% export){
out$average.iid <- matrix(0, nrow = object.n, ncol = length(times))
}
object.modelFrame <- coxModelFrame(object$models[[cause]])
object.modelFrame[, c("strata.num") := as.numeric(.SD$strata)-1]
## recover strata in the training dataset
M.object.strata.num <- do.call(cbind,lapply(1:nCause, function(iCause){
as.numeric(coxStrata(object$models[[iCause]], data = NULL,
strata.vars = ls.infoVar[[iCause]]$stratavars))-1
}))
object.Ustrata <- do.call(paste0, as.data.frame(M.object.strata.num))
## collapse strata variables into on variable
level.strata <- unique(new.strata)
new.Ustrata <- do.call(paste0, as.data.frame(new.strata))
level.Ustrata <- unique(new.Ustrata)
nStrata <- length(level.Ustrata)
for(iStrata in 1:nStrata){ # iStrata <- 1
# {{{ prepare arguments
nTime <- length(times)
iStrataTheCause <- new.strata[iStrata,cause]
indexStrataTheCause <- which(new.Ustrata==level.Ustrata[iStrata])
ls.args <- list()
ls.args$seqTau <- times
ls.args$jumpTime <- object.time
ls.args$jumpTheCause <- object.time %in% object.modelFrame[status==1&strata.num==iStrataTheCause,stop]
ls.args$IFbeta <- lapply(object$iid, function(x){x$IFbeta})
ls.args$cif <- cif[indexStrataTheCause,,drop=FALSE]
ls.args$iS0 <- do.call(cbind,lapply(object$iid, function(x){
x$calcIFhazard$delta_iS0[[iStrataTheCause+1]]
}))
ls.args$newEXb <- eXb[indexStrataTheCause,,drop=FALSE]
ls.args$sampleEXb <- exp(do.call(cbind,lapply(object$models, coxLP, data = NULL, center = FALSE)))
ls.args$sampleTime <- object.modelFrame[["stop"]]
ls.args$indexJump <- matrix(NA, ncol = nCause, nrow = nEtimes)
ls.args$indexSample <- matrix(NA, ncol = nCause, nrow = object.n)
ls.args$Ehazard0 <- vector(mode = "list", length = nCause)
ls.args$cumEhazard0 <- vector(mode = "list", length = nCause)
ls.args$cumhazard_iS0 <- vector(mode = "list", length = nCause)
ls.args$hazard_iS0 <- vector(mode = "list", length = nCause)
ls.args$X <- vector(mode = "list", length = nCause)
ls.args$sameStrata <- matrix(NA,nrow = object.n, ncol = nCause)
ls.args$cumhazard0 <- vector(mode = "list", length = nCause)
ls.args$hazard0 <- vector(mode = "list", length = nCause)
for(iterC in 1:nCause){ # iterC <- 1
iStrataCause <- level.strata[iStrata,iterC]
ls.args$indexJump[,iterC] <- prodlim::sindex(object$iid[[iterC]]$calcIFhazard$time1[[iStrataCause + 1]],
eval.times = object.time)
ls.args$indexSample[,iterC] <- prodlim::sindex(object$iid[[iterC]]$calcIFhazard$time1[[iStrataCause + 1]],
eval.times = object.modelFrame[["stop"]])
ls.args$cumEhazard0[[iterC]] <- object$iid[[iterC]]$calcIFhazard$cumElambda0[[iStrataCause + 1]]
ls.args$Ehazard0[[iterC]] <- object$iid[[iterC]]$calcIFhazard$Elambda0[[iStrataCause + 1]]
ls.args$cumhazard_iS0[[iterC]] <- c(0,object$iid[[iterC]]$calcIFhazard$cumLambda0_iS0[[iStrataCause + 1]])## add 0 to match prodlim
ls.args$hazard_iS0[[iterC]] <- c(0,object$iid[[iterC]]$calcIFhazard$lambda0_iS0[[iStrataCause + 1]]) ## add 0 to match prodlim
ls.args$X[[iterC]] <- new.LPdata[[iterC]][indexStrataTheCause,,drop=FALSE]
ls.args$sameStrata[,iterC] <- M.object.strata.num[,iterC]==iStrataCause
ls.args$hazard0[[iterC]] <- hazard[[iterC]][,iStrataCause + 1]
ls.args$cumhazard0[[iterC]] <- cumhazard[[iterC]][,iStrataCause + 1]
}
# }}}
ls.args$theCause <- cause-1
ls.args$firstJumpTime <- object$iid[[cause]]$etime1.min[iStrataTheCause+1]
ls.args$lastSampleTime <- object.modelFrame[strata.num==iStrataCause,max(.SD$stop)]
ls.args$nTau <- nTime
ls.args$nJump <- nEtimes
ls.args$nNewObs <- length(indexStrataTheCause)
ls.args$nSample <- object.n
ls.args$nCause <- nCause
ls.args$p <- nVar
ls.args$survtype <- surv.type=="survival"
ls.args$exportSE<- ("se" %in% export)
ls.args$exportIF <- ("iid" %in% export)
ls.args$exportIFsum <- ("average.iid" %in% export)
resCpp <- do.call(calcSeCif_cpp, args = ls.args)
if("se" %in% export){
out$se[indexStrataTheCause,] <- resCpp$se
}
if("iid" %in% export){
out$iid[indexStrataTheCause,,] <- resCpp$iid
}
if("average.iid" %in% export){
out$average.iid <- out$average.iid + resCpp$iidsum/new.n
}
}
# }}}
}else{
# {{{ other method
nTimes <- length(times)
sindex.times <- prodlim::sindex(object.time, eval.times = times)-1 ## i.e. -1 is before the first jump
for(iCause in 1:nCause){ ## remove attributes to have a list of matrices
attr(new.LPdata[[iCause]],"assign") <- NULL
attr(new.LPdata[[iCause]],"contrasts") <- NULL
}
if("iid" %in% export || "se" %in% export){
out <- calcSeCif2_cpp(ls_IFbeta = lapply(object$iid,"[[","IFbeta"),
ls_X = new.LPdata,
ls_cumhazard = cumhazard,
ls_hazard = hazard[[cause]],
ls_IFcumhazard = lapply(object$iid,"[[","IFcumhazard"),
ls_IFhazard = object$iid[[cause]]$IFhazard,
eXb = eXb,
nJumpTime = nEtimes, JumpMax = object.maxtime,
tau = times, tauIndex = sindex.times, nTau = nTimes,
nObs = object.n,
theCause = (cause-1), nCause = nCause, hazardType = (surv.type=="hazard"), nVar = nVar,
nNewObs = new.n, strata = new.strata,
exportSE = "se" %in% export, exportIF = "iid" %in% export, exportIFsum = "average.iid" %in% export)
if("iid" %in% export){
out$iid <- aperm(out$iid, c(1,3,2))
}
} else if("average.iid" %in% export){
new.level.strata <- unique(new.strata)
new.level.Ustrata <- apply(new.level.strata,1,paste0,collapse="")
new.n.strata <- length(new.level.Ustrata)
new.Ustrata <- apply(new.strata,1,paste0,collapse="")
new.indexStrata <- lapply(new.level.Ustrata, function(iStrata){
which(new.Ustrata==iStrata) - 1
})
if(is.null(attr(export,"factor"))){
rm.list <- TRUE
factor <- matrix(1, nrow = new.n, ncol = 1)
}else{
rm.list <- FALSE
factor <- attr(export, "factor")
}
outRcpp <- calcAIFcif_cpp(hazard1 = hazard[[cause]],
ls_cumhazard = cumhazard,
ls_tX = lapply(new.LPdata,t),
eXb = eXb,
ls_IFbeta = lapply(object$iid,"[[","IFbeta"),
ls_IFhazard = object$iid[[cause]]$IFhazard,
ls_IFcumhazard = lapply(object$iid,"[[","IFcumhazard"),
nCause = nCause, theCause = cause-1, hazardType = (surv.type == "hazard"),
nJumpTime = nEtimes, JumpMax = tapply(object.maxtime,new.Ustrata,max),
tau = times, tauIndex = sindex.times, nTau = nTimes,
nObs = object.n, nNewObs = new.n,
levelStrata = new.level.strata, nStrata = new.n.strata, ls_indexStrata = new.indexStrata,
nVar = nVar,
factor = factor)
if(rm.list){
out <- list(average.iid = matrix(outRcpp[[1]], nrow = object.n, ncol = nTimes))
}else{
out <- list(average.iid = lapply(outRcpp, function(iMat){matrix(iMat, nrow = object.n, ncol = nTimes)}))
}
}
# }}}
}
return(out)
}
#----------------------------------------------------------------------
### calcSeCSC.R ends here
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