martingaleResid: Martingale Residuals

In discSurv: Discrete Time Survival Analysis

Martingale Residuals

Description

Estimates the martingale residuals of discrete survival model.

Usage

martingaleResid(hazards, dataSetLong)

## S3 method for class '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

Details

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

Value

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

Author(s)

Thomas Welchowski [e-mail]

References

\insertRef

tutzModelDiscdiscSurv

\insertReftherneauMartdiscSurv

See Also

glm

Examples

# 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)

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