predErrCurveCompRisks: Prediction Error Curves For Competing Risks

In discSurv: Discrete Time Survival Analysis

Prediction Error Curves For Competing Risks

Description

Estimates prediction error curves for discrete survival competing risks models

Usage

predErrCurveCompRisks(
  hazards,
  testDataShort,
  trainDataShort,
  timeColumn,
  timeAsFactor = FALSE,
  eventColumns,
  eventColumnsAsFactor = FALSE,
  tmax = NULL
)

## S3 method for class 'discSurvPredErrDiscCompRisks'
plot(x, ...)

Arguments

hazards	Predicted hazard rates on the test data with model fitted on training data (class "matrix"). Predictions are stored in the rows and the number of columns equal the number of events.
testDataShort	Test data in short format (class "data.frame").
trainDataShort	Train data in short format (class "data.frame").
timeColumn	Character giving the column name of the observed times (class "character").
timeAsFactor	Should the time intervals be coded as factor (class "logical")? Default is FALSE. In the default settings the column is treated as quantitative variable (class "numeric").
eventColumns	Character vector giving the column names of the event indicators without censoring column (class "character").
eventColumnsAsFactor	Should the argument eventColumns be interpreted as column name of a factor variable (class "logical")? Default is FALSE.
tmax	Gives the maximum time interval for which prediction errors are calculated (class "integer"). It must not be higher than the maximum observed time in the training data.
x	Object of class "discSurvPredErrDiscCompRisks"
...	Specification of additional arguments in function plot.

Value

Calculated prediction errors for each competing event. Array with one matrix per competing event, with the predictions in the rows and the time points in the columns.

Author(s)

Moritz Berger [e-mail]

References

\insertRef

heyardValCompRisksdiscSurv

See Also

intpredErrCurveCompRisks, predErrCurve

Examples

###########################
# Example unemployment data
library(Ecdat)
data(UnempDur)

# Select subsample
selectInd1 <- 1:200
selectInd2 <- 201:400
trainSet <- UnempDur[which(UnempDur$spell %in% (1:10))[selectInd1], ]
testSet <- UnempDur[which(UnempDur$spell %in% (1:10))[selectInd2], ]

# Convert to long format 
trainSet_long <- dataLongCompRisks(dataShort=trainSet, timeColumn="spell", 
eventColumns=c("censor1", "censor4"), timeAsFactor=TRUE)
tmax          <- max(trainSet$spell)
testSet_long <- dataLongCompRisks(dataShort=testSet, timeColumn="spell", 
eventColumns=c("censor1", "censor4"), aggTimeFormat = TRUE, lastTheoInt=tmax,
timeAsFactor=TRUE)

# Estimate continuation ratio model with logit link
vglmFit <- VGAM::vglm(formula=cbind(e0, e1, e2) ~ timeInt + age + logwage, 
data=trainSet_long, family=VGAM::multinomial(refLevel="e0"))

# Calculate predicted hazards
predHazards <- VGAM::predictvglm(vglmFit, newdata=testSet_long, type="response")

# Compute prediction error 
predErrCurveCompRisks0 <- predErrCurveCompRisks(
hazards=predHazards[,-1], 
testDataShort=testSet, trainDataShort=trainSet, 
timeColumn="spell", eventColumns=c("censor1", "censor4"), 
tmax=tmax)

# Graphics
plot(predErrCurveCompRisks0)

discSurv documentation built on April 29, 2026, 9:07 a.m.