see inline. On Thu, Mar 17, 2011 at 4:58 AM, Rubén Roa <r...@azti.es> wrote: > Hi Alexx, > > I don't see any problem in comparing models based on different distributions > for the same data using the AIC, as long as they have a different number of > parameters and all the constants are included. > For example, you can compare distribution mixture models with different > number of components using the AIC. > This is one example: > Roa-Ureta. 2010. A Likelihood-Based Model of Fish Growth With Multiple Length > Frequency Data. Journal of Biological, Agricultural and Environmental > Statistics 15:416-429. > Here is another example: > www.education.umd.edu/EDMS/fac/Dayton/PCIC_JMASM.pdf > Prof. Dayton writes above that one advantage of AIC over hypothesis testing > is: > "(d) Considerations related to underlying distributions for random > variables can be > incorporated into the decision-making process rather than being treated > as an assumption whose > robustness must be considered (e.g., models based on normal densities > and on log-normal > densities can be compared)."
My reading of this is that AIC can be used to compare models with densities relative to the same dominating measure. Kjetil > Last, if you read Akaike's theorem you will see there is nothing precluding > comparing models built on different distributional models. Here it is: > " the expected (over the sample space and the space of parameter estimates) > maximum log-likelihood of some data on a working model overshoots the > expected (over the sample space only) maximum log-likelihood of the data > under the true model that > generated the data by exactly the number of parameters in the working model." > A remarkable result. > > Rubén > > -----Original Message----- > From: r-help-boun...@r-project.org on behalf of Alexx Hardt > Sent: Wed 3/16/2011 7:42 PM > To: r-help@r-project.org > Subject: Re: [R] R² for non-linear model > > Am 16.03.2011 19:34, schrieb Anna Gretschel: >> Am 16.03.2011 19:21, schrieb Alexx Hardt: >>> And to be on-topic: Anna, as far as I know anova's are only useful to >>> compare a submodel (e.g. with one less regressor) to another model. >>> >> thanks! i don't get it either what they mean by fortune... > > It's an R-package (and a pdf [1]) with collected quotes from the mailing > list. > Be careful with the suggestion to use AIC. If you wanted to compare two > models using AICs, you need the same distribution (that is, > Verteilungsannahme) in both models. > To my knowledge, there is no way to "compare" a gaussian model to an > exponential one (except common sense), but my knowledge is very limited. > > [1] http://cran.r-project.org/web/packages/fortunes/vignettes/fortunes.pdf > > -- > alexx@alexx-fett:~$ vi .emacs > > ______________________________________________ > R-help@r-project.org 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. > > > [[alternative HTML version deleted]] > > > ______________________________________________ > R-help@r-project.org 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. > > ______________________________________________ R-help@r-project.org 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.
