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pec

plot.pec

Plotting prediction error curves

Description

Plotting prediction error curves for one or more prediction models.

Usage

## S3 method for class 'pec'
plot(
  x,
  what,
  models,
  xlim = c(x$start, x$minmaxtime),
  ylim = c(0, 0.3),
  xlab = "Time",
  ylab,
  axes = TRUE,
  col,
  lty,
  lwd,
  type,
  smooth = FALSE,
  add.refline = FALSE,
  add = FALSE,
  legend = ifelse(add, FALSE, TRUE),
  special = FALSE,
  ...
)

Arguments

`x`	Object of class `pec` obtained with function `pec`.
`what`	The name of the entry in `x`. Defauls to `PredErr` Other choices are `AppErr`, `BootCvErr`, `Boot632`, `Boot632plus`.
`models`	Specifies models in `x$models` for which the prediction error curves are drawn. Defaults to all models.
`xlim`	Plotting range on the x-axis.
`ylim`	Plotting range on the y-axis.
`xlab`	Label given to the x-axis.
`ylab`	Label given to the y-axis.
`axes`	Logical. If `FALSE` no axes are drawn.
`col`	Vector of colors given to the curves of `models` in the order determined by `models`.
`lty`	Vector of lty's given to the curves of `models` in the order determined by `models`.
`lwd`	Vector of lwd's given to the curves of `models` in the order determined by `models`.
`type`	Plotting type: either `"l"` or `"s"`, see `lines`.
`smooth`	Logical. If `TRUE` the plotting type for lines is `'l'` else `'s'`.
`add.refline`	Logical. If `TRUE` a dotted horizontal line is drawn as a symbol for the naive rule that predicts probability .5 at all cutpoints (i.e. time points in survival analysis).
`add`	Logical. If `TRUE` only lines are added to an existing device
`legend`	if TRUE a legend is plotted by calling the function legend. Optional arguments of the function `legend` can be given in the form `legend.x=val` where x is the name of the argument and val the desired value. See also Details.
`special`	Logical. If `TRUE` the bootstrap curves of `models` are plotted together with `predErr` of `models` by invoking the function `Special`. Optional arguments of the function `Special` can be given in the form `special.x=val` as with legend. See also Details.
`...`	Extra arguments that are passed to `plot`.

Details

From version 2.0.1 on the arguments legend.text, legend.args, lines.type, lwd.lines, specials are obsolete and only available for backward compatibility. Instead arguments for the invoked functions legend, axis, Special are simply specified as legend.lty=2. The specification is not case sensitive, thus Legend.lty=2 or LEGEND.lty=2 will have the same effect. The function axis is called twice, and arguments of the form axis1.labels, axis1.at are used for the time axis whereas axis2.pos, axis1.labels, etc. are used for the y-axis.

These arguments are processed via ...{} of plot.pec and inside by using the function resolveSmartArgs. Documentation of these arguments can be found in the help pages of the corresponding functions.

Value

The (invisible) object.

Author(s)

Ulla B. Mogensen ulmo@biostat.ku.dk, Thomas A. Gerds tag@biostat.ku.dk

Examples

# simulate data
# with a survival response and two predictors
library(prodlim)
library(survival)
set.seed(280180)
dat <- SimSurv(100)

# fit some candidate Cox models and
# compute the Kaplan-Meier estimate 

Models <- list("Kaplan.Meier"=survfit(Surv(time,status)~1,data=dat),
               "Cox.X1"=coxph(Surv(time,status)~X1,data=dat,x=TRUE,y=TRUE),
               "Cox.X2"=coxph(Surv(time,status)~X2,data=dat,x=TRUE,y=TRUE),
               "Cox.X1.X2"=coxph(Surv(time,status)~X1+X2,data=dat,x=TRUE,y=TRUE))
Models <- list("Cox.X1"=coxph(Surv(time,status)~X1,data=dat,x=TRUE,y=TRUE),
               "Cox.X2"=coxph(Surv(time,status)~X2,data=dat,x=TRUE,y=TRUE),
               "Cox.X1.X2"=coxph(Surv(time,status)~X1+X2,data=dat,x=TRUE,y=TRUE))


# compute the .632+ estimate of the generalization error 
set.seed(17100)
PredError.632plus <- pec(object=Models,
                         formula=Surv(time,status)~X1+X2,
                         data=dat,
                         exact=TRUE,
                         cens.model="marginal",
                         splitMethod="boot632plus",
                         B=5,
                         keep.matrix=TRUE,
                         verbose=TRUE)

# plot the .632+ estimates of the generalization error 
plot(PredError.632plus,xlim=c(0,30))

# plot the bootstrapped curves, .632+ estimates of the generalization error
# and Apparent error for the Cox model 'Cox.X1' with the 'Cox.X2' model
# as benchmark
plot(PredError.632plus,
     xlim=c(0,30),
     models="Cox.X1",
     special=TRUE,
     special.bench="Cox.X2",
     special.benchcol=2,
     special.addprederr="AppErr")

pec

Prediction Error Curves for Risk Prediction Models in Survival Analysis

v2020.11.17

GPL (>= 2)

Authors

Thomas A. Gerds

Initial release