vcdExtra (version 0.5-2)

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 glm or loglm models This implementation is experimental, and is subject to change.

Usage

summarise(object, ...)

## S3 method for class 'glm':
summarise(object, ..., test = NULL)
## S3 method for class 'glmlist':
summarise(object, ..., test = NULL, sortby=NULL)

## S3 method for class 'loglm':
summarise(object, ...)
## S3 method for class 'loglmlist':
summarise(object, ..., sortby=NULL)

Arguments

object, ...

objects of class glm, typically the result of a call to glm, or a list of objects for the glmlist method. Alternatively, objects of class loglm or a "loglmlist" object

test

Not used in the current implementation.

sortby

For glmlist and loglmlist objects, either a numeric or character string specifying the column in the result for which the rows are sorted (in decreasing order).

Value

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

See Also

glmlist, loglmlist, 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)
                
# make a glmlist
mods <- glmlist(indep, coleff, roweff, linlin)
summarise(mods)