modelfit {COUNT}

Fit Statistics for generalized linear models

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)