Nothing
### autoplot.predictCox.R ---
##----------------------------------------------------------------------
## author: Brice Ozenne
## created: feb 17 2017 (10:06)
## Version:
## last-updated: aug 31 2023 (11:19)
## By: Brice Ozenne
## Update #: 1277
##----------------------------------------------------------------------
##
### 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$newdata) ## can be NULL
if(!is.null(newdata) && reduce.data[[1]]==TRUE){
test <- unlist(newdata[,lapply(.SD, function(col){length(unique(col))==1})])
if(any(test)){
newdata[, (names(test)[test]):=NULL]
}
}
status <- NULL
if(first.derivative && ci){
if(is.null(object$vcov[[type]])){
stop("Set argument \'iid\' to TRUE when calling predictCox to be able to display confidence intervals for the first derivative of the ",type,".\n")
}
attr(first.derivative,"vcov") <- object$vcov[[type]]
}
}
dataL <- predict2melt(outcome = object[[type]], ci = ci, band = band,
outcome.lower = if(ci){object[[paste0(type,".lower")]]}else{NULL},
outcome.upper = if(ci){object[[paste0(type,".upper")]]}else{NULL},
outcome.lowerBand = if(band){object[[paste0(type,".lowerBand")]]}else{NULL},
outcome.upperBand = if(band){object[[paste0(type,".upperBand")]]}else{NULL},
newdata = newdata,
status = status,
strata = object$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==1] ; Sigma <- attr(first.derivative,"vcov")[[1]]
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\")"))),
size = 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
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.