envelope.sglg: envelope.sglg

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

envelope.sglg

Description

Build a Normal probability plot with simulated envelope for a deviance-type residuals in semiparametric or multiple linear generalized log-gamma regression models.

Usage

envelope.sglg(fit, Rep)

Arguments

fit	an object of the class sglg. This object is returned from the call to glg(), sglg().
Rep	a positive integer. This is the number of replications on which to build the simulated envelope. Default is Rep=50.

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.

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

Ortega, E., Paula, G. A. and Bolfarine, H. (2008) Deviance residuals in generalized log-gamma regression models with censored observations. Journal of Statistical Computation and Simulation, 78, 747-764.

Examples

rows <- 120
columns <- 2
t_beta  <- c(0.5, 2)
t_sigma <- 0.5
t_lambda <- 1
set.seed(8142031)
x1 <- rbinom(rows, 1, 0.5)
x2 <- runif(columns, 0, 1)
X <- cbind(x1,x2)
error <- rglg(rows, 0, 1, t_lambda)
y1 <- X %*%t_beta + t_sigma * error
data.example <- data.frame(y1,X)
fit <- glg(y1 ~ x1 + x2 - 1,data=data.example)
envelope.sglg(fit,Rep=50)

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