gammahetero1: Classic gamma regression. Log link for the mean

In Gammareg: Classic Gamma Regression: Joint Modeling of Mean and Shape Parameters

Description

Performs the Classic Gamma Regression for joint modeling of mean and shape parameters.

Usage

gammahetero1(formula1, formula2)

Arguments

formula1	object of class formula. It describes yi and xi for the mean equation of the gamma regression.
formula2	object of class formula. It describes zi for the shape equation of the gamma regression.

Details

The classic gamma regression allow the joint modeling of mean and shape parameters of a gamma distributed variable, as is proposed in Cepeda (2001), using the Fisher Scoring algorithm, with log link for the mean and log link for the shape.

Value

object of class Gammareg with the following:

X	object of class matrix, with the variables for modelling the mean.
Z	object of class matrix, with the variables for modelling the shape.
beta	object of class matrix with the estimated coefficients of beta.
gamma	object of class matrix with the estimated coefficients of gamma.
ICB	object of class matrix with the estimated confidence intervals of beta.
ICG	object of class matrix with the estimated confidence intervals of gamma.
CovarianceMatrixbeta	object of class matrix with the estimated covariances of beta.
CovarianceMatrixgamma	object of class matrix with the estimated covariances of gamma.
AIC	the AIC criteria.
iteration	numbers of iterations to convergence.
convergence	value of convergence obtained.

Author(s)

Martha Corrales [e-mail] Edilberto Cepeda-Cuervo [e-mail]

References

1. Cepeda-Cuervo, E. (2001). Modelagem da variabilidade em modelos lineares generalizados. Unpublished Ph.D. tesis. Instituto de Matem<e1>ticas. Universidade Federal do R<ed>o do Janeiro. //http://www.docentes.unal.edu.co/ecepedac/docs/MODELAGEM20DA20VARIABILIDADE.pdf. http://www.bdigital.unal.edu.co/9394/. 2. McCullagh, P. and Nelder, N.A. (1989). Generalized Linear Models. Second Edition. Chapman and Hall.

Examples

# Simulation Example

X1 <- rep(1,500)
X2 <- log(runif(500,0,30))
X3 <- log(runif(500,0,15))
X4 <- log(runif(500,10,20))
mui <- exp(-5 + 0.2*X2 - 0.03*X3)
alphai <- exp(0.2 + 0.1*X2 + 0.3*X4)
Y <- rgamma(500,shape=alphai,scale=mui/alphai)
X <- cbind(X1,X2,X3)
Z <- cbind(X1,X2,X4)
formula.mean= Y~X2+X3
formula.shape= ~X2+X4
a=gammahetero1(formula.mean,formula.shape)
a

Gammareg documentation built on July 8, 2020, 7:01 p.m.