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#' Drawing bootstrapped cross-validation curves and the .632 or .632plus error
#' of models. The prediction error for an optional benchmark model can be added
#' together with bootstrapped cross-validation error and apparent errors.
#'
#' This function is invoked and controlled by \code{plot.pec}.
#'
#' This function should not be called directly. The arguments can be specified
#' as \code{Special.arg} in the call to \code{plot.pec}.
#'
#' @param x an object of class 'pec' as returned by the \code{pec} function.
#' @param y Prediction error values.
#' @param addprederr Additional prediction errors. The options are bootstrap
#' cross-validation errors or apparent errors.
#' @param models One model also specified in \code{pec} for which the
#' \code{predErr} in \code{plot.pec} is to be drawn.
#' @param bench A benchmark model (also specified in \code{pec}) for which the
#' \code{predErr} in \code{plot.pec} is to be drawn.
#' @param benchcol Color of the benchmark curve.
#' @param times Time points at which the curves must be plotted.
#' @param maxboot Maximum number of bootstrap curves to be added. Default is
#' all.
#' @param bootcol Color of the bootstrapped curves. Default is 'gray77'.
#' @param col Color of the different error curves for \code{models}.
#' @param lty Line type of the different error curves for \code{models}.
#' @param lwd Line width of the different error curves for \code{models}.
#' @return Invisible object.
#' @seealso \code{\link{plot.pec}}
Special <- function(x,
y,
addprederr,
models,
bench,
benchcol,
times,
maxboot,
bootcol,
col,
lty,
lwd){
if(length(models) != 1)
stop("Need to choose one and only one 'models'")
at <- prodlim::sindex(x$time,times)
# add the bootstrap curves
boot <- x$BootstrapCrossValErrMat[[models]]
if(is.null(maxboot)) maxboot <- NCOL(boot)
nix <- lapply(1:maxboot,function(b){
lines(times,boot[b,at,drop=TRUE],col=bootcol,type="s",lwd=2.5)
})
# add the predErr of models
addw <- lines(times, y[[models]][at], col=col[1], lty=lty[1], lwd=lwd[1])
# add the xtra chosen whats of models
if(!is.null(addprederr)){
nx <- length(addprederr)
addx <- lapply(1:nx, function(a){
newy <- x[[addprederr[a]]][[models]]
lines(times, newy[at], col=col[a+1], lwd=lwd[a+1], lty=lty[a+1])
})
}
# add the benchmark models
if(bench!= FALSE){
addb <- lines(times, y=y[[bench]][at], col=benchcol, lwd=lwd[1])
}
}
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