On 17/12/2013, at 19:25 PM, Sol Noetinger wrote: > Hello Jari, > Thank you so much for your help. Yes, I have used percent data. > I just know tried with the absolute counts and got, what I guess are right, > values of AIC and BIC. > > Now coming back to the similar values, how can I discriminate in between the > models with so similar AIC and BIC (and Deviance) values. > I apologise if you wrote it in the paragraph below, but I did not understand. > Sol,
What people usually do is that they pick the model with lowest AIC or BIC. If there is not much differences in AIC/BIC, there is not much difference in the models (and often even the curves look similar although they are found with different model equations). You better read on model selection with AIC/BIC. Cheers, Jari Oksanen > I copy here the new results: > > Cluster I > RAD models, family poisson > No. of species 24, total abundance 166 > > par1 par2 par3 > Deviance AIC BIC > Null > 91.6634 161.7168 161.7168 > Preemption 0.23969 53.4299 > 125.4832 126.6613 > Lognormal 0.71216 1.6811 13.3666 87.4199 > 89.7761 > Zipf 0.42497 -1.4264 2.4005 > 76.4538 78.8100 > Mandelbrot 0.42497 -1.4264 2.9057e-06 2.4005 78.4538 81.9880 > > Cluster II > RAD models, family poisson > No. of species 35, total abundance 228 > > par1 par2 par3 Deviance AIC BIC > Null 58.597 > 165.650 165.650 > Preemption 0.13664 45.830 154.884 156.439 > Lognormal 1.0417 1.3473 10.898 121.951 125.062 > Zipf 0.27724 -1.0959 11.181 122.234 125.344 > Mandelbrot 0.3957 -1.221 0.4 10.721 123.774 128.440 > > > Thanks again! > Sol > > On Dec 17, 2013, at 1:48 PM, Jari Oksanen <jari.oksa...@oulu.fi> wrote: > >> Hello, >> On 17/12/2013, at 17:01 PM, Sol Noetinger wrote: >> >>> Hello, >>> >>> Cluster II >>> RAD models, family poisson >>> No. of species 35, total abundance 100 >>> >>> par1 par2 par3 Deviance AIC BIC >>> Null 25.7004 Inf Inf >>> Preemption 0.1 27.8760 Inf Inf >>> Lognormal 0.21756 1.3473 4.7797 Inf Inf >>> Zipf 0.27724 -1.0959 4.9038 Inf Inf >>> Mandelbrot 0.64175 -1.3825 1 4.9181 Inf Inf >>> >>> I read from the manual that to see which models fits better you use the AIC >>> values. >>> What is the meaning of getting "infinite"? >> >> It seems you can get infinite AIC and BIC when the distribution you selected >> is in conflict with the nature of your data. For instance, when you >> postulate a Poisson model (like in your case), but your data are not >> integers (counts). Was that the case with you? Distribution families that go >> along with non-integer (real) data are gaussian and Gamma. You can neither >> use quasipoisson nor other quasi models, because these do not have AIC. >> >>> Can I use the Deviance value to compare the models? >>> And in case I can use the deviance, since there are very close values, >>> should I run a test to see if the differences are significant?� in that >>> case, which one?. >> >> The models have different numbers of estimated parameters and they are not >> nested. Many people claim that you can use neither deviance nor AIC (and >> they are right). At least you must take into account the number of estimated >> parameters that varies from zero to three. >> >> Cheers, Jari Oksanen >> > _______________________________________________ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology
