intPredErr: Integrated prediction error

In discSurv: Discrete Time Survival Analysis

Integrated prediction error

Description

Computes the integrated prediction error curve for discrete survival models.

Usage

intPredErr(
  hazards,
  testTime,
  testEvent,
  trainTime,
  trainEvent,
  testDataLong,
  tmax = NULL
)

Arguments

hazards	Predicted discrete hazards in the test data ("numeric vector").
testTime	Discrete time intervals in short format of the test set ("integer vector").
testEvent	Events in short format in the test set ("binary vector").
trainTime	Discrete time intervals in short format of the training data set ("integer vector").
trainEvent	Events in short format in the training set ("binary vector").
testDataLong	Test data in long format("class data.frame"). The discrete survival function is calculated based on the predicted hazards. It is assumed that the data was preprocessed with a function with prefix "dataLong", see e. g. dataLong, dataLongTimeDep.
tmax	Gives the maximum time interval for which prediction errors are calculated ("integer vector"). It must be smaller than the maximum observed time in the training data of the object produced by function. The default setting NULL means, that all observed intervals are used.

Value

Integrated prediction error ("numeric vector").

Author(s)

Thomas Welchowski [e-mail]

References

\insertRef

tutzModelDiscdiscSurv

\insertRefgneitingPropScorediscSurv

See Also

predErrCurve, aggregate

Examples

##########################
# Example with cancer data

library(survival)
head(cancer)

# Data preparation and convertion to 30 intervals
cancerPrep <- cancer
cancerPrep$status <- cancerPrep$status-1
intLim <- quantile(cancerPrep$time, prob = seq(0, 1, length.out = 30))
intLim [length(intLim)] <- intLim [length(intLim)] + 1

# Cut discrete time in smaller number of intervals
cancerPrep <- contToDisc(dataShort = cancerPrep, timeColumn = "time", intervalLimits = intLim)

# Generate training and test data
set.seed(753)
TrainIndices <- sample (x = 1:dim(cancerPrep) [1], size = dim(cancerPrep) [1] * 0.75)
TrainCancer <- cancerPrep [TrainIndices, ]
TestCancer <- cancerPrep [-TrainIndices, ]
TrainCancer$timeDisc <- as.numeric(as.character(TrainCancer$timeDisc))
TestCancer$timeDisc <- as.numeric(as.character(TestCancer$timeDisc))

# Convert to long format
LongTrain <- dataLong(dataShort = TrainCancer, timeColumn = "timeDisc", eventColumn = "status",
timeAsFactor=FALSE)
LongTest <- dataLong(dataShort = TestCancer, timeColumn = "timeDisc", eventColumn = "status",
timeAsFactor=FALSE)
# Convert factors
LongTrain$timeInt <- as.numeric(as.character(LongTrain$timeInt))
LongTest$timeInt <- as.numeric(as.character(LongTest$timeInt))
LongTrain$sex <- factor(LongTrain$sex)
LongTest$sex <- factor(LongTest$sex)

# Estimate, for example, a generalized, additive model in discrete survival analysis
library(mgcv)
gamFit <- gam (formula = y ~ s(timeInt) + s(age) + sex + ph.ecog, data = LongTrain, 
family = binomial())
summary(gamFit)

# 1. Specification of predicted discrete hazards
# Estimate survival function of each person in the test data
testPredHaz <- predict(gamFit, newdata = LongTest, type = "response")

# 2. Calculate integrated prediction error
intPredErr(hazards = testPredHaz, 
testTime = TestCancer$timeDisc, testEvent = TestCancer$status, 
trainTime = TrainCancer$timeDisc, trainEvent = TrainCancer$status, 
testDataLong = LongTest)

discSurv documentation built on March 18, 2022, 7:12 p.m.