estSurv: Estimated Survival Function

In discSurv: Discrete Time Survival Analysis

Estimated Survival Function

Description

Estimates the survival function S(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

estSurv(haz)

Arguments

haz	Estimated hazard rates ("numeric vector")

Details

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

Value

Estimated probabilities of survival ("numeric vector")

Note

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

Author(s)

Thomas Welchowski [e-mail]

References

\insertRef

tutzModelDiscdiscSurv

See Also

estMargProb

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")
SurvivalFuncCondX <- estSurv(c(hazard, 1))
SurvivalFuncCondX

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