shape: shape

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

shape

Description

Tool that supports the estimation of the shape parameter in semi-parametric or multiple linear accelerated failure time model with generalized log-gamma errors under the presence of censored data. The estimation is based on the profiled likelihood function for the shape parameter of the model.

Usage

shape(formula, npc, data, interval, semi, step)

Arguments

formula	a symbolic description of the systematic component of the model to be fitted.
npc	a data frame with potential nonparametric variables of the systematic part of the model to be fitted.
data	a data frame which contains the variables in the model.
interval	an optional numerical vector of length 2. In this interval is the maximum likelihood estimate of the shape parameter of the model. By default is [0.1,1.5].
semi	a logical value. TRUE means that the model has a non-parametric component. By default is FALSE.
step	an optional positive value. This parameter represents the length of the step of the partition of the interval parameter. By default is 0.1.

Author(s)

Carlos Alberto Cardozo Delgado <[e-mail]>

References

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

Examples

rows  <- 200
columns <- 2
t_beta  <- c(0.5, 2)
t_sigma <- 1
t_lambda <- 1
set.seed(8142031)
x1 <- rbinom(rows, 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(rows, scale = s, shape = a)
cens.time <- rweibull(rows, 0.3, 14)
delta     <- ifelse(t_ini1 > cens.time, 1, 0)
obst1 = t_ini1
for (i in 1:rows) {
if (delta[i] == 1) {
   obst1[i] = cens.time[i]
  }
}
example <- data.frame(obst1,delta,X)
lambda <- shape(Surv(log(obst1),delta) ~ x1 + x2 - 1, data=example)
lambda
# To change interval or step or both options
lambda <- shape(Surv(log(obst1),delta) ~ x1 + x2 - 1, data=example, interval=c(0.95,1.3), step=0.05)
lambda

sglg documentation built on Sept. 4, 2022, 9:05 a.m.