plotsurv.sglg: Plot simultaneously the Kaplan-Meier and parametric...

In sglg: Fitting Semi-Parametric Generalized log-Gamma Regression Models

Plot simultaneously the Kaplan-Meier and parametric estimators of the survival function.

Description

plotsurv.sglg is used to plot simultaneously the Kaplan-Meier and parametric estimators of the survival function.

Usage

plotsurv.sglg(fit)

Arguments

fit	an object of the class sglg. This object is returned from the call to survglg() or ssurvglg().

Author(s)

Carlos Alberto Cardozo Delgado <[e-mail]>

References

Carlos A. Cardozo, G. Paula and L. Vanegas. Semi-parametric accelerated failure time models with generalized log-gamma erros. In preparation.

Carlos Alberto Cardozo Delgado, Semi-parametric generalized log-gamma regression models. Ph. D. thesis. Sao Paulo University.

Examples

require(survival)
n  <- 240
columns <- 2
t_beta  <- c(0.5, 2)
t_sigma <- 1
t_lambda <- 1
set.seed(8142031)
x1 <- rbinom(n, 1, 0.5)
x2 <- runif(columns, 0, 1)
X <- cbind(x1,x2)
s         <- t_sigma^2
a         <- 1/s
t_ini1    <- exp(X %*% t_beta) * rgamma(n, scale = s, shape = a)
cens.time <- rweibull(n, 0.6, 14)
delta1     <- ifelse(t_ini1 > cens.time, 1, 0)
obst1 <- t_ini1
obst1[delta1==1] <- cens.time[delta1==1]
data <- data.frame(obst1,delta1,X)
lambda <- shape(Surv(log(obst1),delta1) ~ x1 + x2 - 1, data=data, interval=c(0.85,0.95), step=0.01)
fit3 <- survglg(Surv(log(obst1),delta1) ~ x1 + x2 - 1, data=data, shape=lambda)
plotsurv.sglg(fit3)

sglg documentation built on Jan. 15, 2026, 1:06 a.m.