### autoplot.predictCSC.R ---
#----------------------------------------------------------------------
## author: Brice Ozenne
## created: feb 27 2017 (10:47)
## Version:
## last-updated: feb 24 2021 (23:07)
## By: Brice Ozenne
## Update #: 109
#----------------------------------------------------------------------
##
### Commentary:
##
### Change Log:
#----------------------------------------------------------------------
##
### Code:
## * autoplot.predictCSC (documentation)
#' @title Plot Predictions From a Cause-specific Cox Proportional Hazard Regression
#' @description Plot predictions from a Cause-specific Cox proportional hazard regression.
#' @name autoplot.predictCSC
#'
#' @param object Object obtained with the function \code{predictCox}.
#' @param ci [logical] If \code{TRUE} display the confidence intervals for the predictions.
#' @param band [logical] If \code{TRUE} display the confidence bands for the predictions.
#' @param group.by [character] The grouping factor used to color the prediction curves. Can be \code{"row"}, \code{"strata"}, or \code{"covariates"}.
#' @param reduce.data [logical] If \code{TRUE} only the covariates that does take indentical values for all observations are displayed.
#' @param plot [logical] Should the graphic be plotted.
#' @param digits [integer] Number of decimal places.
#' @param smooth [logical] Should a smooth version of the risk function be plotted instead of a simple function?
#' @param alpha [numeric, 0-1] Transparency of the confidence bands. Argument passed to \code{ggplot2::geom_ribbon}.
#' @param ... Additional parameters to cutomize the display.
#'
#' @return Invisible. A list containing:
#' \itemize{
#' \item plot: the ggplot object.
#' \item data: the data used to create the plot.
#' }
#'
#' @seealso
#' \code{\link{predict.CauseSpecificCox}} to compute risks based on a CSC model.
## * autoplot.predictCSC (examples)
#' @examples
#' library(survival)
#' library(rms)
#' library(ggplot2)
#' library(prodlim)
#'
#' #### simulate data ####
#' set.seed(10)
#' d <- sampleData(1e2, outcome = "competing.risks")
#' seqTau <- c(0,unique(sort(d[d$event==1,time])), max(d$time))
#'
#' #### CSC model ####
#' m.CSC <- CSC(Hist(time,event)~ X1 + X2 + X6, data = d)
#'
#' pred.CSC <- predict(m.CSC, newdata = d[1:2,], time = seqTau, cause = 1, band = TRUE)
#' autoplot(pred.CSC, alpha = 0.2)
#'
#' #### stratified CSC model ####
#' m.SCSC <- CSC(Hist(time,event)~ strata(X1) + strata(X2) + X6,
#' data = d)
#' pred.SCSC <- predict(m.SCSC, time = seqTau, newdata = d[1:4,],
#' cause = 1, keep.newdata = TRUE, keep.strata = TRUE)
#' autoplot(pred.SCSC, group.by = "strata")
## * autoplot.predictCSC (code)
#' @rdname autoplot.predictCSC
#' @method autoplot predictCSC
#' @export
autoplot.predictCSC <- function(object,
ci = object$se,
band = object$band,
plot = TRUE,
smooth = FALSE,
digits = 2,
alpha = NA,
group.by = "row",
reduce.data = FALSE,
...){
## initialize and check
group.by <- match.arg(group.by, c("row","covariates","strata", names(object$newdata)))
if(group.by[[1]] == "covariates" && ("newdata" %in% names(object) == FALSE)){
stop("argument \'group.by\' cannot be \"covariates\" when newdata is missing in the object \n",
"set argment \'keep.newdata\' to TRUE when calling the predictCox function \n")
}
if(group.by[[1]] == "strata" && ("strata" %in% names(object) == FALSE)){
stop("argument \'group.by\' cannot be \"strata\" when strata is missing in the object \n",
"set argment \'keep.strata\' to TRUE when calling the predictCox function \n")
}
if(ci[[1]] && (object$se[[1]]==FALSE || is.null(object$conf.level))){
stop("argument \'ci\' cannot be TRUE when no standard error have been computed \n",
"set arguments \'se\' and \'confint\' to TRUE when calling the predict.CauseSpecificCox function \n")
}
if(band[[1]] && (object$band[[1]]==FALSE || is.null(object$conf.level))){
stop("argument \'band\' cannot be TRUE when the quantiles for the confidence bands have not been computed \n",
"set arguments \'band\' and \'confint\' to TRUE when calling the predict.CauseSpecificCox function \n")
}
if(any(rank(object$times) != 1:length(object$times))){
stop("Invalid object. The prediction times must be strictly increasing \n")
}
## dots <- list(...)
## if(length(dots)>0){
## txt <- names(dots)
## txt.s <- if(length(txt)>1){"s"}else{""}
## stop("unknown argument",txt.s,": \"",paste0(txt,collapse="\" \""),"\" \n")
## }
## display
newdata <- copy(object$newdata)
if(!is.null(newdata) && reduce.data[[1]]){
test <- unlist(newdata[,lapply(.SD, function(col){length(unique(col))==1})])
if(any(test)){
newdata[, (names(test)[test]):=NULL]
}
}
dataL <- predict2melt(outcome = object$absRisk, ci = ci, band = band,
outcome.lower = if(ci){object$absRisk.lower}else{NULL},
outcome.upper = if(ci){object$absRisk.upper}else{NULL},
outcome.lowerBand = if(band){object$absRisk.lowerBand}else{NULL},
outcome.upperBand = if(band){object$absRisk.upperBand}else{NULL},
newdata = newdata,
status = NULL,
strata = object$strata,
times = object$times,
name.outcome = "absRisk",
group.by = group.by,
digits = digits
)
gg.res <- predict2plot(dataL = dataL,
name.outcome = "absRisk", # must not contain space to avoid error in ggplot2
ci = ci,
band = band,
group.by = group.by,
conf.level = object$conf.level,
alpha = alpha,
smooth = smooth,
xlab = "time",
ylab = "absolute risk",
...
)
if(plot){
print(gg.res$plot)
}
return(invisible(gg.res))
}
#----------------------------------------------------------------------
### autoplot.predictCSC.R ends here
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