[R] multinom() residual deviance

[Sascha Vieweg]

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