influence.sglg: influence

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

influence

Description

influence.sglg extracts from a object of class sglg the local influence measures and displays their graphs versus the index of the observations.

Usage

## S3 method for class 'sglg'
influence(model, ...)

Arguments

model	an object of the class sglg. This object is returned from the call to glg(), sglg(), survglg() or ssurvglg().
...	other arguments.

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.

Examples

rows <- 100
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)
error <- rglg(rows, 0, 1, t_lambda)
y1 <- X %*%t_beta + t_sigma * error
data.example <- data.frame(y1,X)
fit1 <- glg(y1 ~ x1 + x2 - 1,data=data.example)
influence(fit1)

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