survglg: Fitting linear generalized log-gamma regression models under...

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

Fitting linear generalized log-gamma regression models under the presence of right censored data.

Description

survglg is used to fit a multiple linear regression model in which the response variable is continuous, strictly positive, asymmetric and there are right censored observations. In this setup, the location parameter of the logarithm of the response variable is modeled by a linear model of the parameters.

Usage

survglg(formula, data, shape, Maxiter, Tolerance)

Arguments

formula	a symbolic description of the systematic component of the model to be fitted. See details for further information.
data	an optional data frame, list containing the variables in the model.
shape	an optional value for the shape parameter of the model.
Maxiter	an optional positive integer giving the maximal number of iterations for the estimating process. Default value is 1e03.
Tolerance	an optional positive value, which represents the convergence criterion. Default value is 1e-04.

Value

mu a vector of parameter estimates asociated with the location parameter.

sigma estimate of the scale parameter associated with the model.

lambda estimate of the shape parameter associated with the model.

interval estimate of a 95% confidence interval for each estimate parameters associated with the model.

Deviance the deviance associated with the model.

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.

Cardozo C.A., Paula G., and Vanegas L. (2022). Generalized log-gamma additive partial linear models with P-spline smoothing. Statistical Papers.

Examples

require(survival)
rows  <- 240
columns <- 2
t_beta  <- c(0.5, 2)
t_sigma <- 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)
delta1     <- ifelse(t_ini1 > cens.time, 1, 0)
obst1 <- t_ini1
for (i in 1:rows) {
if (delta1[i] == 1) {
   obst1[i] <- cens.time[i]
  }
}
data.example <- data.frame(obst1,delta1,X)
fit3 <- survglg(Surv(log(obst1),delta1) ~ x1 + x2 - 1, data=data.example,shape=0.9)
logLik(fit3)
summary(fit3)

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