gumbel: Gumbel Link Function

In discSurv: Discrete Time Survival Analysis

Gumbel Link Function

Description

Constructs the link function with gumbel distribution in approriate format for use in generalized, linear models.

Usage

gumbel()

Details

Insert this function into a binary regression model

Author(s)

Thomas Welchowski [e-mail]

Matthias Schmid [e-mail]

References

\insertRef

tutzModelDiscdiscSurv

See Also

glm

Examples

# Example with copenhagen stroke study
library(pec)
data(cost)
head(cost)

# Take subsample and convert time to months
costSub <- cost [1:50, ]
costSub$time <- ceiling(costSub$time/30)
costLong <- dataLong(dataShort = costSub, timeColumn = "time", eventColumn = "status",
timeAsFactor=TRUE)
gumbelModel <- glm(formula = y ~ timeInt + diabetes, data = costLong, 
family = binomial(link = gumbel()))

# Estimate hazard given prevStroke and no prevStroke
hazPrevStroke <- predict(gumbelModel, newdata=data.frame(timeInt = factor(1:143), 
diabetes = factor(rep("yes", 143), levels = c("no", "yes"))), type = "response")
hazWoPrevStroke <- predict(gumbelModel, newdata = data.frame(timeInt = factor(1:143), 
diabetes=factor(rep("no", 143), levels = c("no", "yes"))), type = "response")

# Estimate survival function
SurvPrevStroke <- cumprod(1 - hazPrevStroke)
SurvWoPrevStroke <- cumprod(1 - hazWoPrevStroke)

# Example graphics of survival curves with and without diabetes
plot(x = 1:143, y = SurvWoPrevStroke, type = "l", xlab = "Months", 
ylab = "S (t|x)", las = 1, lwd = 2, ylim = c(0,1))
lines(x = 1:143, y = SurvPrevStroke, col = "red", lwd = 2)
legend("topright", legend = c("Without diabetes", "Diabetes"), 
lty = 1, lwd =2, col = c("black", "red"))

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