### autoplot.predictCox.R ---
##----------------------------------------------------------------------
## author: Brice Ozenne
## created: feb 17 2017 (10:06)
## Version:
## last-updated: Oct 16 2024 (09:24)
## By: Brice Ozenne
## Update #: 1299
##----------------------------------------------------------------------
##
### Commentary:
##
### Change Log:
##----------------------------------------------------------------------
##
### Code:
## * autoplot.predictCox (documentation)
#' @title Plot Predictions From a Cox Model
#' @description Plot predictions from a Cox model.
#' @name autoplot.predictCox
#'
#' @param object Object obtained with the function \code{predictCox}.
#' @param type [character] The type of predicted value to display.
#' Choices are:
#' \code{"hazard"} the hazard function,
#' \code{"cumhazard"} the cumulative hazard function,
#' or \code{"survival"} the survival function.
#' @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 plot [logical] Should the graphic be plotted.
#' @param digits [integer] Number of decimal places when displaying the values of the covariates in the caption.
#' @param alpha [numeric, 0-1] Transparency of the confidence bands. Argument passed to \code{ggplot2::geom_ribbon}.
#' @param ylab [character] Label for the y axis.
#' @param smooth [logical] Should a smooth version of the risk function be plotted instead of a simple function?
#' @param first.derivative [logical] If \code{TRUE}, display the first derivative over time of the risks/risk differences/risk ratios.
#' (confidence intervals are obtained via simulation).
#' @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 ... 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{predictCox}} to compute cumulative hazard and survival based on a Cox model.
## * autoplot.predictCox (examples)
#' @examples
#' library(survival)
#' library(ggplot2)
#'
#' #### simulate data ####
#' set.seed(10)
#' d <- sampleData(1e2, outcome = "survival")
#' seqTau <- c(0,sort(unique(d$time[d$event==1])), max(d$time))
#'
#' #### Cox model ####
#' m.cox <- coxph(Surv(time,event)~ X1 + X2 + X3,
#' data = d, x = TRUE, y = TRUE)
#'
#' ## display baseline hazard
#' e.basehaz <- predictCox(m.cox)
#' autoplot(e.basehaz, type = "cumhazard")
#' \dontrun{
#' autoplot(e.basehaz, type = "cumhazard", size.point = 0) ## without points
#' autoplot(e.basehaz, type = "cumhazard", smooth = TRUE)
#' autoplot(e.basehaz, type = "cumhazard", smooth = TRUE, first.derivative = TRUE)
#' }
#'
#' ## display baseline hazard with type of event
#' \dontrun{
#' e.basehaz <- predictCox(m.cox, keep.newdata = TRUE)
#' autoplot(e.basehaz, type = "cumhazard")
#' autoplot(e.basehaz, type = "cumhazard", shape.point = c(3,NA))
#' }
#'
#' ## display predicted survival
#' \dontrun{
#' pred.cox <- predictCox(m.cox, newdata = d[1:2,],
#' times = seqTau, type = "survival", keep.newdata = TRUE)
#' autoplot(pred.cox)
#' autoplot(pred.cox, smooth = TRUE)
#' autoplot(pred.cox, group.by = "covariates")
#' autoplot(pred.cox, group.by = "covariates", reduce.data = TRUE)
#' autoplot(pred.cox, group.by = "X1", reduce.data = TRUE)
#' }
#'
#' ## predictions with confidence interval/bands
#' \dontrun{
#' pred.cox <- predictCox(m.cox, newdata = d[1:2,,drop=FALSE],
#' times = seqTau, type = "survival", band = TRUE, se = TRUE, keep.newdata = TRUE)
#' res <- autoplot(pred.cox, ci = TRUE, band = TRUE, plot = FALSE)
#' res$plot + facet_wrap(~row)
#' res2 <- autoplot(pred.cox, ci = TRUE, band = TRUE, alpha = 0.1, plot = FALSE)
#' res2$plot + facet_wrap(~row)
#' }
#'
#' #### Stratified Cox model ####
#' \dontrun{
#' m.cox.strata <- coxph(Surv(time,event)~ strata(X1) + strata(X2) + X3 + X4,
#' data = d, x = TRUE, y = TRUE)
#'
#' ## baseline hazard
#' pred.baseline <- predictCox(m.cox.strata, keep.newdata = TRUE, type = "survival")
#' res <- autoplot(pred.baseline)
#' res$plot + facet_wrap(~strata, labeller = label_both)
#'
#' ## predictions
#' pred.cox.strata <- predictCox(m.cox.strata, newdata = d[1:3,,drop=FALSE],
#' time = seqTau, keep.newdata = TRUE, se = TRUE)
#'
#' res2 <- autoplot(pred.cox.strata, type = "survival", group.by = "strata", plot = FALSE)
#' res2$plot + facet_wrap(~strata, labeller = label_both) + theme(legend.position="bottom")
#'
#' ## smooth version
#' autoplot(pred.cox.strata, type = "survival", group.by = "strata", smooth = TRUE, ci = FALSE)
#' }
#'
#' #### Cox model with splines ####
#' \dontrun{
#' require(splines)
#' m.cox.spline <- coxph(Surv(time,event)~ X1 + X2 + ns(X6,4),
#' data = d, x = TRUE, y = TRUE)
#' grid <- data.frame(X1 = factor(0,0:1), X2 = factor(0,0:1),
#' X6 = seq(min(d$X6),max(d$X6), length.out = 100))
#' pred.spline <- predictCox(m.cox.spline, newdata = grid, keep.newdata = TRUE,
#' se = TRUE, band = TRUE, centered = TRUE, type = "lp")
#' autoplot(pred.spline, group.by = "X6")
#' autoplot(pred.spline, group.by = "X6", alpha = 0.5)
#'
#' grid2 <- data.frame(X1 = factor(1,0:1), X2 = factor(0,0:1),
#' X6 = seq(min(d$X6),max(d$X6), length.out = 100))
#' pred.spline <- predictCox(m.cox.spline, newdata = rbind(grid,grid2), keep.newdata = TRUE,
#' se = TRUE, band = TRUE, centered = TRUE, type = "lp")
#' autoplot(pred.spline, group.by = c("X6","X1"), alpha = 0.5, plot = FALSE)$plot + facet_wrap(~X1)
#' }
## * autoplot.predictCox (code)
#' @rdname autoplot.predictCox
#' @method autoplot predictCox
#' @export
autoplot.predictCox <- function(object,
type = NULL,
ci = object$se,
band = object$band,
plot = TRUE,
smooth = NULL,
digits = 2,
alpha = NA,
group.by = "row",
reduce.data = FALSE,
ylab = NULL,
first.derivative = FALSE,
...){
## initialize and check
possibleType <- c("cumhazard","survival","lp")
possibleType <- possibleType[possibleType %in% names(object)]
nVar.lp <- length(object$var.lp)
if(is.null(type)){
if(length(possibleType) == 1){
type <- possibleType
}else{
stop("argument \'type\' must be specified to choose between ",paste(possibleType, collapse = " "),"\n")
}
}else{
type <- match.arg(type, possibleType)
}
if(is.null(smooth)){
if(type == "lp"){
smooth <- 0.5
}else{
smooth <- FALSE
}
}
if(is.null(ylab)){
if(first.derivative){
ylab <- switch(type,
"cumhazard" = if(object$baseline && nVar.lp>0){"instantaneous baseline hazard"}else{"instantaneous hazard"},
"survival" = if(object$baseline && nVar.lp>0){"derivative of the baseline survival"}else{"derivative of the survival"})
}else{
ylab <- switch(type,
"lp" = "linear predictor",
"cumhazard" = if(object$baseline && nVar.lp>0){"cumulative baseline hazard"}else{"cumulative hazard"},
"survival" = if(object$baseline && nVar.lp>0){"baseline survival"}else{"survival"})
}
}
if(type=="lp"){
group.by <- match.arg(group.by, object$var.lp, several.ok = TRUE)
}else{
if(length(group.by)>1){
stop("Argument \'group.by\' must have length 1.\n")
}
group.by <- match.arg(group.by, c("row","covariates","strata",object$var.lp,object$var.strata))
}
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]] %in% c("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(group.by[[1]] %in% c(object$var.lp,object$var.strata) && ("newdata" %in% names(object) == FALSE)){
stop("argument \'group.by\' cannot be \"",group.by[[1]],"\" when newdata is missing in the object \n",
"set argment \'keep.newdata\' to TRUE when calling the predictCox function \n")
}
if(!is.null(object$newdata$strata) && !is.null(object$var.strata)){
if(any(object$var.strata %in% colnames(object$newdata))){
stop("cannot handle covariate \"",paste(object$var.strata[object$var.strata %in% colnames(object$newdata)],collapse ="\" \""),"\" as this name is used internally.\n",
"consider refit the model using another name for the covariate. \n")
}
splitStrata <- do.call(rbind,strsplit(split = ",", x = as.character(object$newdata$strata), fixed = TRUE))
colnames(splitStrata) <- object$infoVar$stratavars.original
object$newdata <- cbind(object$newdata,splitStrata)
}
if(ci[[1]]==TRUE && (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 predictCox 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 predictCox function \n")
}
if(object$nTimes!= 0 && any(rank(object$times) != 1:length(object$times))){
stop("Invalid object. The prediction times must be strictly increasing \n")
}
## dots <- list(...)
## if(length(dots)>0){
## txt <- names(dots)
## txt.s <- if(length(txt)>1){"s"}else{""}
## stop("unknown argument",txt.s,": \"",paste0(txt,collapse="\" \""),"\" \n")
## }
## ** reshape data
if(type == "lp"){
if(first.derivative){
stop("Argument \'first.derivative\' should be FALSE when argument \'type\' equals \"lp\". \n")
}
if(length(object$var.lp)==0){
stop("No covariate in the linear predictor so nothing to display.\n")
}
if(group.by[1] %in% object$var.lp == FALSE){
stop("The first element of argument \'group.by\' should refer to one of the covariates: \"",paste(object$var.lp, collapse= "\" \""),"\".\n")
}
if("time" %in% group.by){
stop("The argument \'group.by\' should not contain \"time\" as this name is used internally.\n")
}
if("row" %in% group.by){
stop("The argument \'group.by\' should not contain \"row\" as this name is used internally.\n")
}
if("lowerCI" %in% group.by){
stop("The argument \'group.by\' should not contain \"lowerCI\" as this name is used internally.\n")
}
if("upperCI" %in% group.by){
stop("The argument \'group.by\' should not contain \"upperCI\" as this name is used internally.\n")
}
dataL <- data.table::as.data.table(object)
dataL$lp.smooth <- dataL$lp
dataL[["time"]] <- dataL[[group.by[1]]]
dataL[["row"]] <- 1
if(ci){
data.table::setnames(dataL, old = c("lp.lower","lp.upper"), new = c("lowerCI","upperCI"))
dataL$lowerCI.smooth <- dataL$lowerCI
dataL$upperCI.smooth <- dataL$upperCI
}
if(band){
data.table::setnames(dataL, old = c("lp.lowerBand","lp.upperBand"), new = c("lowerBand","upperBand"))
dataL$lowerBand.smooth <- dataL$lowerBand
dataL$upperBand.smooth <- dataL$upperBand
}
object$infoVar$time <- group.by[1]
group.by <- if(length(group.by[-1])==0){"row"}else{group.by[-1]}
}else{
if(!is.matrix(object[[type]])){
## baseline hazard/survival
if(is.null(object[["strata"]])){
object[[type]] <- rbind(object[[type]])
if(object$nTimes==0){
if(0 %in% object$times == FALSE){
if(type=="cumhazard"){
object[[type]] <- cbind(0,object[[type]])
}else if(type=="survival"){
object[[type]] <- cbind(1,object[[type]])
}
object$times <- c(0,object$times)
if(!is.null(object$newdata)){
object$newdata <- rbind(data.table(start = 0, stop = 0, status = NA, strata = 1, strata.num = 0, eXb = NA, statusM1 = NA, XXXindexXXX = NA),
object$newdata)
}
}
if(object$lastEventTime %in% object$times == FALSE){
object[[type]] <- cbind(object[[type]],object[[type]][length(object[[type]])])
object$times <- c(object$times,pmin(object$lastEventTime,max(object$times)+1e-12))
if(!is.null(object$newdata)){
object$newdata <- rbind(data.table(start = 0, stop = object$lastEventTime, status = 0, strata = 1, strata.num = 0, eXb = NA, statusM1 = NA, XXXindexXXX = NA),
object$newdata)
}
}
}
}else{
index.unique <- !duplicated(object$strata)
strata <- object$strata[index.unique]
if(!is.null(attr(object$strata,"covariates"))){
attr(strata,"covariates") <- attr(object$strata,"covariates")[index.unique]
}
n.strata <- length(strata)
time <- unique(sort(object[["times"]]))
n.time <- length(time)
type.tempo <- matrix(NA, nrow = n.strata, ncol = n.time)
init <- switch(type,
"cumhazard" = 0,
"survival" = 1)
for(iStrata in 1:n.strata){ ## iStrata <- 1
index.strata <- which(object[["strata"]]==strata[iStrata])
type.tempo[iStrata,] <- stats::approx(x = object[["times"]][index.strata],
y = object[[type]][index.strata],
yleft = init,
yright = NA,
xout = time,
method = "constant")$y
}
object[[type]] <- type.tempo
object[["strata"]] <- strata
object[["times"]] <- time
}
newdata <- NULL
if(object$nTimes==0){
status <- object$newdata
}else{
status <- NULL
}
}else{
newdata <- data.table::copy(object$newdata) ## can be NULL
if(!is.null(newdata) && reduce.data[[1]]==TRUE){
test <- unlist(newdata[,lapply(.SD, function(col){length(unique(col))==1})])
if(any(test)){
newdata[, (names(test)[test]):=NULL]
}
}
status <- NULL
if(first.derivative && ci){
if(is.null(object[[paste0(type,".iid")]])){
stop("Set argument \'iid\' to TRUE when calling predictCox to be able to display confidence intervals for the first derivative of the ",type,".\n")
}
iIID <- object[[paste0(type,".iid")]]
## compute variance-covariance matrix
attr(first.derivative,"vcov") <- lapply(1:(dim(iIID)[3]), function(iObs){
if(dim(iIID)[2]==1){
if(type=="lp"){
return(sum(iIID[,iObs,]^2))
}else{
return(sum(iIID[,,iObs]^2))
}
}else{ ## crossprod among timepoints where the 'survival' is not only NA
return(crossprod(iIID[,,iObs][,colSums(!is.na(iIID[,,iObs]))>0,drop=FALSE]))
}
})
}
}
dataL <- predict2melt(outcome = object[[type]], ci = ci, band = band,
outcome.lower = if(ci){object[[paste0(type,".lower")]]}else{NULL},
outcome.upper = if(ci){object[[paste0(type,".upper")]]}else{NULL},
outcome.lowerBand = if(band){object[[paste0(type,".lowerBand")]]}else{NULL},
outcome.upperBand = if(band){object[[paste0(type,".upperBand")]]}else{NULL},
newdata = newdata,
status = status,
strata = object$strata,
times = object$times,
name.outcome = type,
group.by = group.by,
digits = digits
)
}
## ** display
gg.res <- predict2plot(dataL = dataL,
name.outcome = type,
ci = ci,
band = band,
group.by = group.by,
conf.level = object$conf.level,
alpha = alpha,
smooth = smooth,
xlab = if(is.null(object$infoVar)){"time"}else{object$infoVar$time},
ylab = ylab,
first.derivative = first.derivative,
...
)
if(plot){
print(gg.res$plot)
}
return(invisible(gg.res))
}
## * predict2melt
predict2melt <- function(outcome, name.outcome,
ci, outcome.lower, outcome.upper,
band, outcome.lowerBand, outcome.upperBand,
newdata, status, strata, times,
group.by, digits){
patterns <- NULL ## [:CRANtest:] data.table
n.time <- NCOL(outcome)
if(!is.null(time)){
time.names <- times
}else{
time.names <- 1:n.time
}
colnames(outcome) <- paste0(name.outcome,"_",time.names)
## merge outcome with CI and band ####
pattern <- paste0(name.outcome,"_")
if(!is.null(status)){
Ustrata <- unique(status$strata)
M.status <- matrix(as.numeric(NA), nrow = NROW(outcome), ncol = NCOL(outcome),
dimnames = list(NULL, paste0("status_",time.names)))
status[,c("index") := match(.SD$stop, times)]
for(iS in 1:length(Ustrata)){ ## iS <- 1
iIndex <- which(status$strata == Ustrata[iS])
iStatus <- status[iIndex, list("nevent" = sum(.SD$status), "index" = unique(.SD$index)),by="stop"]
M.status[iS, iStatus$index] <- (iStatus$nevent>0)
}
## M.status[1,times[]] <- status[strata==Ustrata[1]]
## times
outcome <- cbind(outcome, M.status)
pattern <- c(pattern,"status")
}
if(ci){
pattern <- c(pattern,"lowerCI_","upperCI_")
colnames(outcome.lower) <- paste0("lowerCI_",time.names)
colnames(outcome.upper) <- paste0("upperCI_",time.names)
}
if(band){
pattern <- c(pattern,"lowerBand_","upperBand_")
colnames(outcome.lowerBand) <- paste0("lowerBand_",time.names)
colnames(outcome.upperBand) <- paste0("upperBand_",time.names)
}
outcome <- data.table::data.table(
cbind(outcome,
outcome.lower, outcome.upper,
outcome.lowerBand,outcome.upperBand)
)
## merge with covariates ####
outcome[, row := 1:.N]
keep.col <- "row"
if(!is.null(newdata)){ ## predicted survival/risk/average treatment effect
cov.names <- names(newdata)
## used individually
newdata <- newdata[, (cov.names) := lapply(cov.names,function(col){
if (is.numeric(.SD[[col]]))
round(.SD[[col]],digits) else .SD[[col]]})]
outcome <- cbind(outcome,newdata)
## keep name of the covariate for when merging all covariates together
newdata <- newdata[, (cov.names) := lapply(cov.names,function(col){
paste0(col,"=",.SD[[col]])})]
outcome[, c("covariates") := interaction(newdata,sep = " ")]
keep.col <- c(keep.col,if(!is.null(cov.names)){"covariates"},cov.names)
}
if(!is.null(strata)){ ## baseline survival/risk
outcome[, strata := strata]
if(!is.null(attr(strata,"covariates"))){
outcome[,c(names(attr(strata,"covariates"))) := attr(strata,"covariates") ]
}
keep.col <- c(keep.col,"strata",names(attr(strata,"covariates")))
}
## reshape to long format ####
dataL <- data.table::melt(outcome, id.vars = keep.col,
measure = patterns(pattern),
variable.name = "time", value.name = gsub("_","",pattern))
dataL[, time := as.numeric(as.character(factor(time, labels = time.names)))]
dataL <- dataL[!is.na(dataL[[name.outcome]])]
return(dataL)
}
## * predict2plot
predict2plot <- function(dataL, name.outcome,
ci, band, group.by, smooth,
conf.level, alpha, xlab, ylab,
smoother = NULL, formula.smoother = NULL, first.derivative = FALSE,
size.estimate = 1.5, size.point = 3, size.ci = 1.1, size.band = 1.1, shape.point = c(3,18), n.sim = 250){
.GRP <- NULL ## [:: for CRAN CHECK::]
if(first.derivative && (smooth==FALSE)){
stop("Set argument \'smooth\' to TRUE when \'first.derivative\' is TRUE. \n")
}
dataL <- data.table::copy(dataL)
## set at t- the value of t-1
vec.outcome <- name.outcome
if(ci){
vec.outcome <- c(vec.outcome,"lowerCI","upperCI")
}
if(band){
vec.outcome <- c(vec.outcome,"lowerBand","upperBand")
}
group.by2 <- unique(c(group.by,"row"))
dataL[,c("timeRight") := c(.SD$time[2:.N]-1e-12,.SD$time[.N]+1e-12), by = group.by2]
dataL[,c(group.by) := as.factor(.SD[[group.by]])]
## smooth ####
if(smooth>=1){
requireNamespace("mgcv",quietly=FALSE)
if(is.null(smoother)){
tol <- 1e-12
test.increasing <- all(na.omit(dataL[,diff(.SD[[name.outcome]]),by="row"][[2]])>=-tol)
test.decreasing <- all(na.omit(dataL[,diff(.SD[[name.outcome]]),by="row"][[2]])<=tol)
if(!requireNamespace("scam",quietly=TRUE) || (test.increasing==FALSE && test.decreasing == FALSE)){
formula.smoother <- ~s(time)
smoother <- mgcv::gam
}else{
smoother <- function(formula, data){
out <- try(do.call(scam::scam, args = list(formula = formula, data = data)))
if(inherits(x=out,what="try-error")){
out <- do.call(mgcv::gam, args = list(formula = update(formula, ".~s(time)"), data = data))
}
return(out)
}
if(test.increasing){
formula.smoother <- ~s(time, bs = "mpi")
}else if(test.decreasing){
formula.smoother <- ~s(time, bs = "mpd")
}
}
}
if(length(all.vars(formula.smoother))!=1){
stop("Argument \'formula.smoother\' must contain exactly one variable \n")
}
ff <- update(as.formula(paste0(name.outcome,"~.")),formula.smoother)
if(first.derivative){
requireNamespace("numDeriv")
warper <- function(data){ ## data <- dataL[row==1]
iModel <- do.call(smoother, args = list(formula = ff, data = data))
return(numDeriv::grad(function(x){predict(iModel, newdata = data.frame(time = x), type = "response")}, x = data$time))
}
dataL[, c(paste0(name.outcome,".smooth")) := warper(.SD), by = "row"]
if(ci){
warperCI <- function(data, Sigma, n.sim){ ## data <- dataL[row==2] ; Sigma <- attr(first.derivative,"vcov")[[2]]
ls.deriv <- lapply(1:n.sim, function(x){
data2 <- data.table::copy(data)
data2[, c(name.outcome) := mvtnorm::rmvnorm(1, mean = data[[name.outcome]], sigma = Sigma)[1,]]
iModel <- try(do.call(smoother, args = list(formula = ff,data = data2)))
if(inherits(x=iModel,what="try-error")){
return(rep(NA, length(data$time)))
}else{
return(numDeriv::grad(function(x){predict(iModel, newdata = data.frame(time = x), type = "response")}, x = data$time))
}
})
M.CI <- apply(do.call(rbind,ls.deriv), 2, quantile, probs = c((1-conf.level)/2,1-(1-conf.level)/2), na.rm = TRUE)
return(as.data.table(t(M.CI)))
}
dataL[, c("lowerCI.smooth","upperCI.smooth") := warperCI(.SD, Sigma = attr(first.derivative,"vcov")[[.GRP]], n.sim = n.sim), by = "row"]
}
if(band){
stop("Confidence bands are not available when argument \'first.derivative\' is TRUE \n")
}
}else{
dataL[, c(paste0(name.outcome,".smooth")) := do.call(smoother, args = list(formula = ff,data = .SD))$fitted, by = "row"]
if(ci){
ff <- update(as.formula("lowerCI~."),formula.smoother)
dataL[, c("lowerCI.smooth") := do.call(smoother, args = list(formula = ff,data = .SD))$fitted, by = "row"]
ff <- update(as.formula("upperCI~."),formula.smoother)
dataL[, c("upperCI.smooth") := do.call(smoother, args = list(formula = ff,data = .SD))$fitted, by = "row"]
}
if(band){
ff <- update(as.formula("lowerBand~."),formula.smoother)
dataL[, c("lowerBand.smooth") := do.call(smoother, args = list(formula = ff,data = .SD))$fitted, by = "row"]
ff <- update(as.formula("upperBand~."),formula.smoother)
dataL[, c("upperBand.smooth") := do.call(smoother, args = list(formula = ff,data = .SD))$fitted, by = "row"]
}
}
}
## display ####
labelCI <- paste0(conf.level*100,"% pointwise \n confidence interval")
labelBand <- paste0(conf.level*100,"% simulaneous \n confidence interval \n")
gg.base <- ggplot2::ggplot(data = dataL, mapping = ggplot2::aes(group = row))
if(band){ ## confidence band
if(smooth>0){
if(!is.na(alpha)){
gg.base <- gg.base + ggplot2::geom_ribbon(eval(parse(text = paste0(
"ggplot2::aes(x = time, ymin = lowerBand.smooth, ymax = upperBand.smooth, group = ",group.by,")"))),
alpha = alpha)
}else{
gg.base <- gg.base + ggplot2::geom_line(eval(parse(text = paste0(
"ggplot2::aes(x = time, y = lowerBand.smooth, group = ",group.by,", color = ",group.by,", linetype = \"band\")"))),
linewidth = size.band)
gg.base <- gg.base + ggplot2::geom_line(eval(parse(text = paste0(
"ggplot2::aes(x = time, y = upperBand.smooth, group = ",group.by, ", color = ",group.by,", linetype = \"band\")"))),
linewidth = size.band)
}
}else{
if(!is.na(alpha)){
gg.base <- gg.base + ggplot2::geom_rect(ggplot2::aes_string(xmin = "time", xmax = "timeRight", ymin = "lowerBand", ymax = "upperBand",
fill = "labelBand"), linetype = 0, alpha = alpha)
gg.base <- gg.base + scale_fill_manual("", values="grey12")
}else{
gg.base <- gg.base + ggplot2::geom_segment(ggplot2::aes_string(x = "time", y = "lowerBand", xend = "timeRight", yend = "lowerBand", color = "\"band\""),
linewidth = size.band)
gg.base <- gg.base + ggplot2::geom_segment(ggplot2::aes_string(x = "time", y = "upperBand", xend = "timeRight", yend = "upperBand", color = "\"band\""),
linewidth = size.band)
}
}
}
if(ci){ ## confidence interval
if(smooth>0){
if(!is.na(alpha)){
gg.base <- gg.base + ggplot2::geom_errorbar(ggplot2::aes_string(x = "time", ymin = "lowerCI.smooth", ymax = "upperCI.smooth", linetype = "labelCI"),
width = size.ci)
gg.base <- gg.base + ggplot2::scale_linetype_manual("",values=setNames(1,labelCI))
}else{
gg.base <- gg.base + ggplot2::geom_line(eval(parse(text = paste0(
"ggplot2::aes(x = time, y = lowerCI.smooth, group = ",group.by,", color = ",group.by,", linetype = \"ci\")"))),
linewidth = size.ci)
gg.base <- gg.base + ggplot2::geom_line(eval(parse(text = paste0(
"ggplot2::aes(x = time, y = upperCI.smooth, group = ",group.by,", color = ",group.by,", linetype = \"ci\")"))),
linewidth = size.ci)
}
}else{
if(!is.na(alpha)){
gg.base <- gg.base + ggplot2::geom_errorbar(ggplot2::aes_string(x = "time", ymin = "lowerCI", ymax = "upperCI", linetype = "labelCI"),
width = size.ci)
gg.base <- gg.base + ggplot2::scale_linetype_manual("",values=setNames(1,labelCI))
}else{
gg.base <- gg.base + ggplot2::geom_segment(ggplot2::aes_string(x = "time", y = "lowerCI", xend = "timeRight", yend = "lowerCI", color = "\"ci\""),
linewidth = size.ci)
gg.base <- gg.base + ggplot2::geom_segment(ggplot2::aes_string(x = "time", y = "upperCI", xend = "timeRight", yend = "upperCI", color = "\"ci\""),
linewidth = size.ci)
}
}
}
## estimate
if(smooth>0){
gg.base <- gg.base + ggplot2::geom_line(mapping = ggplot2::aes_string(x = "time", y = paste0(name.outcome,".smooth"), group = group.by, color = group.by),
linewidth = size.estimate)
}else{
gg.base <- gg.base + ggplot2::geom_segment(mapping = ggplot2::aes_string(x = "timeRight", y = name.outcome, xend = "time", yend = name.outcome, color = group.by),
linewidth = size.estimate)
if("status" %in% names(dataL)){
dataL$status <- as.character(dataL$status)
gg.base <- gg.base + ggplot2::geom_point(data = na.omit(dataL),
mapping = ggplot2::aes_string(x = "time", y = name.outcome, color = group.by, shape = "status"), size = size.point)
gg.base <- gg.base + ggplot2::scale_shape_manual(breaks = c(0,1), values = shape.point, labels = c("censoring","event"))
}else{
gg.base <- gg.base + ggplot2::geom_point(data = dataL,
mapping = ggplot2::aes_string(x = "time", y = name.outcome, color = group.by), size = size.point)
}
}
if(group.by=="row"){
gg.base <- gg.base + ggplot2::labs(color="observation") + ggplot2::theme(legend.key.height=unit(0.1,"npc"),
legend.key.width=unit(0.08,"npc"))
# display only integer values
uniqueObs <- unique(dataL$row)
if(length(uniqueObs)==1){
gg.base <- gg.base + ggplot2::scale_color_discrete(guide="none")
}
}
if(is.na(alpha)[[1]] && (band[[1]] || ci[[1]])){
indexTempo <- which(c(ci,band)==1)
if(smooth == FALSE){
levels.group.by <- levels(dataL[[group.by]])
n.levels.group.by <- length(levels.group.by)
gg.base <- gg.base + ggplot2::scale_color_manual("", breaks = c(c("ci","band")[indexTempo],levels.group.by),
labels = c(c(labelCI,labelBand)[indexTempo],paste0(group.by," ",levels.group.by)),
values = c(c("grey","black")[indexTempo],
grDevices::hcl(h = seq(15, 375, length = n.levels.group.by + 1), l = 65, c = 100)[1:n.levels.group.by]))
}else{
gg.base <- gg.base + ggplot2::scale_linetype_manual("", breaks = c("ci","band")[indexTempo],
labels = c(labelCI,labelBand)[indexTempo],
values = c("dotdash","longdash")[indexTempo])
}
}else if(ci[[1]] && band[[1]]){
gg.base <- gg.base + ggplot2::guides(linetype = ggplot2::guide_legend(order = 1),
fill = ggplot2::guide_legend(order = 2),
group = ggplot2::guide_legend(order = 3)
)
}
gg.base <- gg.base + ggplot2::xlab(xlab) + ggplot2::ylab(ylab)
if(name.outcome != "lp"){
gg.base <- gg.base + ggplot2::coord_cartesian(xlim = c(0,max(dataL$timeRight)))
}
## export
ls.export <- list(plot = gg.base,
data = dataL)
return(ls.export)
}
#----------------------------------------------------------------------
### autoplot.predictCox.R ends here
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