discSurv (version 1.4.2)

estMargProb: Estimated Marginal Probabilities

Description

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

Usage

estMargProb(haz)

Arguments

haz

Numeric vector of estimated hazard rates.

Value

Named vector of estimated marginal probabilities.

Details

The argument *haz* must be given for the all intervals [a_0, a_1), [a_1, a_2), ..., [a_q-1, a_q), [a_q, Inf).

References

Gerhard Tutz and Matthias Schmid, (2016), Modeling discrete time-to-event data, Springer series in statistics, Doi: 10.1007/978-3-319-28158-2

See Also

estSurv

Examples

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

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

# Convert to long format
UnempLong <- dataLong (dataSet=subUnempDur, timeColumn="spell", censColumn="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!
# }