modelfit: Fit Statistics for generalized linear models

In COUNT: Functions, Data and Code for Count Data

Description

modelfit is used following a glm() or glm.nb() model to produce a list of model fit statistics.

Usage

modelfit(x)

Arguments

x	the only argument is the name of the fitted glm or glm.nb function model

Details

modelfit is to be used as a post-estimation function, following the use of glm() or glm.nb().

Value

obs	number of model observatiions
aic	AIC statistic
xvars	number of model predictors
rdof	residial degrees of freedom
aic_n	AIC, 'aic'/'obs'
ll	log-likelihood
bic_r	BIC - Raftery parameterization
bic_l	BIC - log-likelihood Standard definition (Stata)
bic_qh	Hannan-Quinn IC statistic (Limdep)

Note

modelfit.r must be loaded into memory in order to be effectve. Users may past modelfit.r into script editor to run, as well as load it.

Author(s)

Joseph M. Hilbe, Arizona State University, and Jet Propulsion Laboratory, California Institute of technology

References

Hilbe, J.M. (2011), Negative Binomial Regression, second edition, Cambridge University Press.

Hilbe, J.M. (2009), Logistic Regression Models, Chapman Hall/CRC

See Also

glm, glm.nb

Examples

## Hilbe (2011), Table 9.17
library(MASS)
data(lbwgrp)
nb9_3 <- glm.nb(lowbw ~ smoke + race2 + race3 + offset(log(cases)), data=lbwgrp)
summary(nb9_3)
exp(coef(nb9_3))
modelfit(nb9_3) 

COUNT documentation built on May 2, 2019, 2:37 a.m.