[R] multinom() residual deviance

From: Sascha Vieweg <saschaview_at_gmail.com>
Date: Fri, 08 Apr 2011 17:14:54 +0200 (CEST)


Running a binary logit model on the data

df <- data.frame(y=sample(letters[1:3], 100, repl=T), x=rnorm(100))

reveals some residual deviance:

summary(glm(y ~ ., data=df, family=binomial("logit")))

However, running a multinomial model on that data (multinom, nnet) reveals a residual deviance:

summary(multinom(y ~ ., data=df))

On page 203, the MASS book says that "here the deviance is comparing with the model that correctly predicts each person, not the multinomial response for each cell of the mininal model", followed by and instruction how to compare with the saturated model.

For me as a beginner, this sounds like an important warning, however, I don't know what the warning exactly means and hence do not know what the difference between the residual deviance of the former (binary) and the latter (multinomial) model is.

(I need the deviances to calculate some of the pseudo R-squares with function pR2(), package "pscl".)

Could you give good advice?

Thanks
*S*

-- 
Sascha Vieweg, saschaview_at_gmail.com

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Received on Fri 08 Apr 2011 - 15:31:55 GMT

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