estMargProb: Marginal Probabilities

In discSurv: Discrete Time Survival Analysis

Marginal Probabilities

Description

Estimates the marginal probability P(T=t|x) based on estimated discrete hazards. The discrete hazards may or may not depend on covariates. The covariates have to be equal across all estimated hazard rates. Therefore the given discrete hazards should only vary over time.

Usage

estMargProb(hazards)

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

Arguments

hazards	Estimated discrete hazards (class "numeric")
x	Object of class "discSurvEstMargProb"
...	Specification of additional arguments in function plot.

Details

The argument hazards must be given for all intervals \left[ a_0, a_1 \right), \left[ a_1, a_2 \right), ..., \left[ a_{q-1}, a_q \right), \left[ a_q, \infty \right).

Value

Estimated marginal probabilities (class "numeric")

Note

It is assumed that all time points up to the last interval \left[ a_q, \infty \right) are available. If not already present, these can be added manually.

Author(s)

Thomas Welchowski [e-mail]

References

\insertRef

tutzModelDiscdiscSurv

See Also

estSurv

Examples

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

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

# Convert to long format
UnempLong <- dataLong(dataShort = subUnempDur, timeColumn = "spell", eventColumn = "censor1")
head(UnempLong)

# Estimate binomial model with logit link
Fit <- glm(formula = y ~ timeInt + age + logwage, data = UnempLong, family = binomial())

# Estimate discrete survival function given age, logwage of first person
hazard <- predict(Fit, newdata = subset(UnempLong, obj == 1), type = "response")

# Estimate marginal probabilities given age, logwage of first person
MarginalProbCondX <- estMargProb (c(hazard, 1))
MarginalProbCondX
sum(MarginalProbCondX)==1 # TRUE: Marginal probabilities must sum to 1!

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