[R] Log-likelihood function
r.darnell at uq.edu.au
Wed May 2 12:51:12 CEST 2007
Alternatively generate the log-likelihood using the sum(dpois(y,
fitted(model), log = TRUE))
> You're right. I do need to learn more. I never learned null/residual
> deviance. I know the deviance is equivalent to an anova decompostion.
> But I've never dealt with it seperated like this.
> I understand deviance as the difference between two model's log-likelihood
> difference between them and the most complex. I want to compare two
> models that are not the most complex. That is why I wanted the
> I am using the poisson distribution because my response is count data, so
> the link is the log. If the deviance in R is computed by comparing the
> fitted model against the most saturated (which would make sense). Then
> yes, I can use that. I just picked the log-likelihood because I'm
> comparing two models. And that's the best way. But, it's equivalent if R
> compares the fitted to the most complex.
> I assumed the deviance print out tested the fitted model against the least
> complex. This tests whether the current model parameters can be dropped
> (that's what I thought the null deviance meant). I'm not sure what the
> residaul deviance means though.
> My main concern is why the likelihood functions differed between SAS and
> R. If anyone has encountered this or understands why, I would appreciate
> some help.
> Prof Brian Ripley wrote:
>> I think you need to learn about deviances, which R does print.
>> Log-likelihoods are only defined up to additive constants. In this case
>> the conventional constant differs if you view this as a Poisson or as a
>> product-multinomial log-linear model, and R gives you the log-likelihood
>> for a Poisson log-linear model (assuming you specified family=poisson).
>> However, deviances and differences in log-likelihoods do not depend on
>> More details and worked examples can be found in MASS (the book, see the
>> FAQ), including other ways to fit log-linear models in R.
>> On Tue, 1 May 2007, someone ashamed of his real name wrote:
>>> I've computed a loglinear model on a categorical dataset. I would like
>>> test whether an interaction can be dropped by comparing the
>>> from two models(the model with the interaction vs. the model without).
>>> Since R does not immediately print the log-likelihood when I use the
>>> function, I used SAS initially. After searching for an extracting
>>> I found one in R. But, the log-likelihood given by SAS is different
>>> the one given by R. I'm not sure if the "logLik" function in R is
>>> giving me
>>> something I don't want. Or if I'm misinterpreting the SAS output. Can
>>> anyone help?
>> Brian D. Ripley, ripley at 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
>> R-help at stat.math.ethz.ch mailing list
>> PLEASE do read the posting guide
>> and provide commented, minimal, self-contained, reproducible code.
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