discSurv (version 2.0.0)

plotCumInc: Plot Estimated Cumulative Incidence Function

Description

Generates a plot of an estimated cumulative incidence function P(T <= t, event=k | x) based on estimated hazards of a discrete competing risks model or a discrete subdistribution hazard model.

Usage

plotCumInc(hazards, eventFocus = NULL, ...)

Arguments

hazards

Numeric matrix (where each column represents one event) or vector of estimated hazards("numeric matrix").

eventFocus

Column that represent the primary event ("integer vector"). Only applicable in the case of competing risks.

...

Further arguments passed to plot.

References

tutzModelDiscdiscSurv

See Also

estSurv, estCumInz, compRisksGEE

Examples

# NOT RUN {
# Example with unemployment data
library(Ecdat)
data(UnempDur)

# Select subsample
SubUnempDur <- UnempDur [1:100, ]

################################
# Competing risks model 

# Estimate GEE models for all events
estGEE <- compRisksGEE(datShort = SubUnempDur, dataTransform = "dataLongCompRisks", 
corstr = "independence", formulaVariable =~ timeInt + age + ui + logwage * ui, 
eventColumns = c("censor1", "censor2", "censor3", "censor4"), timeColumn = "spell")

# Estimate hazards of all events given the covariates of third person
SubUnempDurLong <- dataLongCompRisks(dataShort = SubUnempDur, 
eventColumns = c("censor1", "censor2", "censor3", "censor4"), timeColumn = "spell") 
preds <- predict(estGEE, subset(SubUnempDurLong, obj == 3))

plotCumInc(preds, eventFocus = 3)


###############################
# Subdistribution hazards model

# Convert to long format
SubUnempDurLong <- dataLongSubDist(dataShort = SubUnempDur, timeColumn = "spell", 
eventColumns = c("censor1", "censor2", "censor3", "censor4"), eventFocus = "censor1")

# Estimate continuation ratio model with logit link
glmFit <- glm(formula = y ~ timeInt + age + ui + logwage * ui, data = SubUnempDurLong, 
family = binomial(), weights = SubUnempDurLong$subDistWeights)

# Estimated subdistribution hazard given the covariates of the third person
preds <- predict(glmFit, type = "response", newdata = subset(SubUnempDurLong, obj == 3))

plotCumInc(preds)

# }