glm objects, the
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.
loglm models have equivalent glm forms, but the
summary methods give quite different results.
Summarise provides a brief summary for one or more models
fit to the same dataset
nobs methods exist
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a fitted model object for which there exists a logLik method to extract the corresponding log-likelihood
optionally more fitted model objects
saturated model log likelihood reference value (use 0 if deviance is not available)
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
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).
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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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