pec: plotPredictEventProb – R documentation

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pec

plotPredictEventProb

Plotting predicted survival curves.

Description

Ploting time-dependent event risk predictions.

Usage

plotPredictEventProb(
  x,
  newdata,
  times,
  cause = 1,
  xlim,
  ylim,
  xlab,
  ylab,
  axes = TRUE,
  col,
  density,
  lty,
  lwd,
  add = FALSE,
  legend = TRUE,
  percent = FALSE,
  ...
)

Arguments

`x`	Object specifying an event risk prediction model.
`newdata`	A data frame with the same variable names as those that were used to fit the model `x`.
`times`	Vector of times at which to return the estimated probabilities.
`cause`	Show predicted risk of events of this cause
`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 survival curve.
`density`	Densitiy of the color – useful for showing many (overlapping) curves.
`lty`	Vector of lty's given to the survival curve.
`lwd`	Vector of lwd's given to the survival curve.
`add`	Logical. If `TRUE` only lines are added to an existing device
`legend`	Logical. 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.
`percent`	Logical. If `TRUE` the y-axis is labeled in percent.
`...`	Parameters that are filtered by `SmartControl` and then passed to the functions: `plot`, `axis`, `legend`.

Details

Arguments for the invoked functions legend and axis 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 plotPredictEventProb and inside by using the function SmartControl.

Value

The (invisible) object.

Author(s)

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

References

Ulla B. Mogensen, Hemant Ishwaran, Thomas A. Gerds (2012). Evaluating Random Forests for Survival Analysis Using Prediction Error Curves. Journal of Statistical Software, 50(11), 1-23. URL https://www.jstatsoft.org/v50/i11.

Examples

# generate some competing risk data

pec

Prediction Error Curves for Risk Prediction Models in Survival Analysis

v2020.11.17

GPL (>= 2)

Authors

Thomas A. Gerds

Initial release