estMargProb {discSurv}

Estimated 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)

Arguments

hazards

Estimated discrete hazards ("numeric vector")

Details

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

Value

Estimated marginal probabilities ("numeric vector")

Note

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

Author(s)

Thomas Welchowski [e-mail]

References

Tutz G, Schmid M (2016). Modeling discrete time-to-event data. Springer Series in Statistics.

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!