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.
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
>
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