Alternatively generate the log-likelihood using the sum(dpois(y, fitted(model), log = TRUE))
Regards Ross Darnell Doxastic wrote: > > 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 > log-likelihood. > > 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 >> which. >> >> 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 >>> to >>> test whether an interaction can be dropped by comparing the >>> log-likelihoods >>> 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 >>> "glm" >>> function, I used SAS initially. After searching for an extracting >>> function, >>> I found one in R. But, the log-likelihood given by SAS is different >>> from >>> 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, [EMAIL PROTECTED] >> 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@stat.math.ethz.ch mailing list >> https://stat.ethz.ch/mailman/listinfo/r-help >> PLEASE do read the posting guide >> http://www.R-project.org/posting-guide.html >> and provide commented, minimal, self-contained, reproducible code. >> >> > > -- View this message in context: http://www.nabble.com/Log-likelihood-function-tf3678882.html#a10283755 Sent from the R help mailing list archive at Nabble.com. ______________________________________________ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
