sglg (version 0.2.5)

survglg: 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 = 1000, Tolerance = 1e-06)

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.

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 1000.

Tolerance

an optional positive value, which represents the convergence criterion. Default value is 1e-05.

Author

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 errors. 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  <- 200
columns <- 2
t_beta  <- c(0.5, 2)
t_sigma <- 1
set.seed(8142031)
x1 <- rbinom(rows, 1, 0.5)
x2 <- runif(rows, 0, 1)
X <- cbind(x1,x2)
s         <- t_sigma^2
a         <- 1/s
t_ini1    <- exp(X %*% t_beta) * rweibull(rows, scale = s, shape = a)
cens.time <- rweibull(rows, 0.75, 20)
delta1     <- ifelse(t_ini1 > cens.time, 1, 0)
obst1 <- t_ini1
obst1[delta1==1] <- cens.time[delta1==1]
data.example <- data.frame(obst1,delta1,X)
fit3 <- survglg(Surv(log(obst1),delta1) ~ x1 + x2 - 1, data=data.example, shape = 1)
fit3$condition
fit3$scores
summary(fit3)
# We can obtain the logLik (in the exp scale) from
fit3$llgg
delta2 = 1 - delta1
fit4 <- survreg(Surv(obst1,delta2) ~ x1 + x2 - 1, data=data.example, dist = 'weibull')
summary(fit4)