Re: [R] multinom(): likelihood of model?

From: Prof Brian Ripley <ripley_at_stats.ox.ac.uk>
Date: Sat 14 May 2005 - 15:41:08 EST

By definition, the deviance is minus twice the maximized log-likelihood plus a const. In any of these models for discrete data, the saturated model predicts exactly, so the const is zero.

There are worked examples in MASS4, the book multinom() supports.

On Fri, 13 May 2005, Brooks Miner wrote:

> Hi all,
>
> I'm working on a multinomial (or "polytomous") logistic regression using R
> and have made great progress using multinom() from the nnet library. My
> response variable has three categories, and there are two different possible
> predictors. I'd like to use the likelihoods of certain models (ie,
> saturated, fitteds, and null) to calculate Nagelkerke R-squared values for
> various fitted models.
>
> My question today is simple: once I have fitted a model using multinom(), how
> do I find the likelihood (or log likelihood) of my fitted model? I
> understand that this value must be part of the $deviance or $AIC components
> of the fitted model, but my understanding is too limited at this point for me
> to know how to calculate the likelihood of my fitted model from either of
> these outputs.
>
> Thanks in advance to any assistance offered. I'd be happy to provide an
> example of my data and multinom() entries if that would help.
>
> Gratefully,
>
> - Brooks
> ----------------------------
> Brooks Miner
> Research Scientist
> Laird Lab
> UW Biology
> 206.616.9385
> http://protist.biology.washington.edu/Lairdlab/
>
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-- 
Brian D. Ripley,                  ripley@stats.ox.ac.uk
Professor of Applied Statistics,  http://www.stats.ox.ac.uk/~ripley/
University of Oxford,             Tel:  +44 1865 272861 (self)
1 South Parks Road,                     +44 1865 272866 (PA)
Oxford OX1 3TG, UK                Fax:  +44 1865 272595

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Received on Sat May 14 15:45:05 2005

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