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, saschav...@gmail.com
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