discSurv (version 2.0.0)

martingaleResid: Martingale Residuals

Description

Estimates the martingale residuals of discrete survival model.

Usage

martingaleResid(hazards, dataSetLong)
# S3 method for discSurvMartingaleResid
plot(x, covariates, dataSetLong, ...)

Arguments

hazards

Predicted hazards from a discrete survival model ("numeric vector").

dataSetLong

Data in long format ("class data.frame").

x

Object of class "discSurvMartingaleResid"("class discSurvMartingaleResid")

covariates

Names of covariates to plot ("character vector").

…

Additional arguments to the plot function

Value

Martingale residuals for each observation in long format ("numeric vector").

Details

Gives a different plot of each marginal covariate against the martingale residuals. Additionally a nonparametric loess estimation is done.

References

tutzModelDiscdiscSurv therneauMartdiscSurv

See Also

glm

Examples

# NOT RUN {
# Example with cross validation and unemployment data 
library(Ecdat)
data(UnempDur)
summary(UnempDur$spell)

# Extract subset of data
set.seed(635)
IDsample <- sample(1:dim(UnempDur)[1], 100)
UnempDurSubset <- UnempDur [IDsample, ]

# Conversion to long format
UnempDurSubsetLong <- dataLong(dataShort = UnempDurSubset,
timeColumn = "spell", eventColumn = "censor1")

# Estimate discrete survival continuation ratio model
contModel <- glm(y ~ timeInt + age + logwage, data = UnempDurSubsetLong,
family = binomial(link = "logit"))

# Fit hazards to the data set in long format
hazPreds <- predict(contModel, type = "response")

# Calculate martingale residuals for the unemployment data subset
MartResid <- martingaleResid (hazards = hazPreds, dataSetLong = UnempDurSubsetLong)
MartResid
sum(MartResid)

# Plot martingale residuals vs each covariate in the event interval
# Dotted line represents the loess estimate
plot(MartResid, covariates = c("age", "logwage"), dataSetLong = UnempDurSubsetLong)

# }