vcdExtra (version 0.8-5)

Summarise: Brief Summary of Model Fit for glm and loglm Models

Description

For glm objects, the print and summary methods give too much information if all one wants to see is a brief summary of model goodness of fit, and there is no easy way to display a compact comparison of model goodness of fit for a collection of models fit to the same data. All loglm models have equivalent glm forms, but the print and summary methods give quite different results.

Summarise provides a brief summary for one or more models fit to the same dataset for which logLik and nobs methods exist (e.g., glm and loglm models).

Usage

Summarise(object, ...)
# S3 method for glmlist
Summarise(object, ..., saturated = NULL, sortby = NULL)
# S3 method for loglmlist
Summarise(object, ..., saturated = NULL, sortby = NULL)
# S3 method for default
Summarise(object, ..., saturated = NULL, sortby = NULL)

Value

A data frame (also of class anova) with columns c("AIC", "BIC", "LR Chisq", "Df", "Pr(>Chisq)"). Row names are taken from the names of the model object(s).

Arguments

object

a fitted model object for which there exists a logLik method to extract the corresponding log-likelihood

...

optionally more fitted model objects

saturated

saturated model log likelihood reference value (use 0 if deviance is not available)

sortby

either a numeric or character string specifying the column in the result by which the rows are sorted (in decreasing order)

Author

Achim Zeileis

Details

The function relies on residual degrees of freedom for the LR chisq test being available in the model object. This is true for objects inheriting from lm, glm, loglm, polr and negbin.

See Also

logLik, glm, loglm,

logLik.loglm, modFit

Examples

data(Mental)
indep <- glm(Freq ~ mental+ses,
                family = poisson, data = Mental)
Summarise(indep)
Cscore <- as.numeric(Mental$ses)
Rscore <- as.numeric(Mental$mental)

coleff <- glm(Freq ~ mental + ses + Rscore:ses,
                family = poisson, data = Mental)
roweff <- glm(Freq ~ mental + ses + mental:Cscore,
                family = poisson, data = Mental)
linlin <- glm(Freq ~ mental + ses + Rscore:Cscore,
                family = poisson, data = Mental)
                
# compare models
Summarise(indep, coleff, roweff, linlin)