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.
Usage
LRstats(object, ...)
# S3 method for class 'glmlist'
LRstats(object, ..., saturated = NULL, sortby = NULL)
# S3 method for class 'loglmlist'
LRstats(object, ..., saturated = NULL, sortby = NULL)
# Default S3 method
LRstats(object, ..., saturated = NULL, sortby = NULL)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)
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).
Details
LRstats 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). %This implementation is experimental,
and is subject to change.
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
Other glmlist functions:
Kway(),
glmlist(),
mosaic.glmlist()
Examples
data(Mental)
indep <- glm(Freq ~ mental+ses,
family = poisson, data = Mental)
LRstats(indep)
#> Likelihood summary table:
#> AIC BIC LR Chisq Df Pr(>Chisq)
#> indep 209.59 220.19 47.418 15 3.155e-05 ***
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
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
LRstats(indep, coleff, roweff, linlin)
#> Likelihood summary table:
#> AIC BIC LR Chisq Df Pr(>Chisq)
#> indep 209.59 220.19 47.418 15 3.155e-05 ***
#> coleff 179.00 195.50 6.829 10 0.7415
#> roweff 174.45 188.59 6.281 12 0.9013
#> linlin 174.07 185.85 9.895 14 0.7698
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1