gnfit: gnfit

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

gnfit

Description

This function provides some useful statistics to assess the quality of fit of generalized log-gamma probabilistic model, including the statistics Cramer-von Mises and Anderson-Darling. It can also calculate other goodness of fit such as Hannan-Quin Information Criterion and Kolmogorov-Smirnov test.

Usage

gnfit(starts, data)

Arguments

starts	numeric vector. Initial parameters to maximize the likelihood function
data	numeric vector. A sample of a generalized log-gamma distribution.

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

## Not run: 
set.seed(1)
# The size of the sample must be median or large to obtain a good estimates
n <- 100
sample <- rglg(n,location=0,scale=0.5,shape=0.75)
# This step takes a few minutes.
result <- gnfit(starts=c(0.1,0.75,1),data=sample)
result

## End(Not run)

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