plot.pec: Plotting prediction error curves

In pec: Prediction Error Curves for Risk Prediction Models in Survival Analysis

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 [e-mail], Thomas A. Gerds [e-mail]

See Also

pecsummary.pecSpecialprodlim

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 documentation built on April 11, 2023, 5:55 p.m.