# Re: [R] multinom() residual deviance

From: <Bill.Venables_at_csiro.au>
Date: Sat, 09 Apr 2011 13:43:48 +1000

fm <- glm(I(y == "a") ~ x, binomial, df)

which you can check leads to the same result. I.e. this model amalgamates classes "b" and "c" into one.

The second is a multivariate logistic model that considers all three classes defined by your factor y, (and has twice the number of parameters, among other things). The three clases, "a", "b" and "c" remain separate in the model.

Hence the two models are not directly comparable, so why should the deviance be?

Bill Venables.

From: r-help-bounces_at_r-project.org [r-help-bounces_at_r-project.org] On Behalf Of Sascha Vieweg [saschaview_at_gmail.com] Sent: 09 April 2011 01:14
To: r-help_at_r-project.org
Subject: [R] multinom() residual deviance

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".)

Thanks
*S*

```--
Sascha Vieweg, saschaview_at_gmail.com

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