quantile_residuals: Quantile Residuals for a Generalized Log-gamma Regression...

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

Quantile Residuals for a Generalized Log-gamma Regression Model

Description

quantile_residuals is used to generate quantile residuals for a generalized log-gamma regression model.

Usage

quantile_residuals(fit)

Arguments

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

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

# Example 1
n <- 400
set.seed(4)
error <- rglg(n,0,0.5,1)
y <- as.data.frame(0.5 + error)
names(y) <- "y"
fit_0 <- glg(y~1,data=y)
fit_0$mu
fit_0$sigma
fit_0$lambda
quantile_residuals(fit_0)
# Example 2
n <- 500
set.seed(6)
error <- rglg(n,0,0.5,1)
x1 <- runif(n,-2,2)
beta <- c(0.5,2)
y <- cbind(1,x1)%*%beta + error
data <- data.frame(y=y,x1=x1)
fit_1 <- glg(y~x1,data=data)
fit_1$mu
fit_1$sigma
fit_1$lambda
quantile_residuals(fit_1)

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