Brief Summary of Model Fit for glm and loglm Models
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).
Summarise(object, ...) ## S3 method for class 'glmlist' Summarise(object, ..., saturated = NULL, sortby = NULL) ## S3 method for class 'loglmlist' Summarise(object, ..., saturated = NULL, sortby = NULL) ## Default S3 method: Summarise(object, ..., saturated = NULL, sortby = NULL)
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) |
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
.
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).
Achim Zeileis
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)
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