autoplot.predictCSC: Plot Predictions From a Cause-specific Cox Proportional...
In bozenne/riskRegressionLight: Light version of the package riskRegression

Plot predictions from a Cause-specific Cox proportional hazard regression.

## S3 method for class 'predictCSC'
autoplot(object, ci = FALSE, band = FALSE,
  group.by = "row", reduce.data = FALSE, plot = TRUE, digits = 2,
  alpha = NA, ...)

`object`	Object obtained with the function `predictCox`.
`ci`	[logical] If `TRUE` display the confidence intervals for the predictions.
`band`	[logical] If `TRUE` display the confidence bands for the predictions.
`group.by`	[character] The grouping factor used to color the prediction curves. Can be `"row"`, `"strata"`, or `"covariates"`.
`reduce.data`	[logical] If `TRUE` only the covariates that does take indentical values for all observations are displayed.
`plot`	[logical] Should the graphic be plotted.
`digits`	[integer] Number of decimal places.
`alpha`	[numeric, 0-1] Transparency of the confidence bands. Argument passed to `ggplot2::geom_ribbon`.
`...`	Not used. Only for compatibility with the plot method.

library(survival)
library(rms)
library(ggplot2)
library(prodlim)
#### simulate data ####
set.seed(10)
d <- sampleData(1e2, outcome = "competing.risks")

#### CSC model ####
m.CSC <- CSC(Hist(time,event)~ X1 + X2 + X6, data = d)

pred.CSC <- predict(m.CSC, newdata = d[1:2,], time = 1:5, cause = 1)#'
autoplot(pred.CSC)


#### stratified CSC model ####
m.SCSC <- CSC(Hist(time,event)~ strata(X1) + strata(X2) + X6,
              data = d)
pred.SCSC <- predict(m.SCSC, time = 1:3, newdata = d[1:4,],
                     cause = 1, keep.newdata = TRUE, keep.strata = TRUE)
autoplot(pred.SCSC, group.by = "strata")