[R-sig-eco] AIC / BIC vs P-Values / MAM

[Ben Bolker]

On 10-08-04 01:13 PM, Chris Mcowen wrote:
> Hi Ben,
>
> That is great thanks.
>
>    
>> whether you select models via p-value or AIC *should* be based on whether you are trying to test hypotheses or make predictions
>>      
> I have 7 factors of which 5 have been shown, theoretically and empirically, to have an impact on my response variable. The other two are somewhat wild shots, but i have a hunch they are important too.
>
> The problem is there are no clear analytical patterns of the variables, they don't fit into neat boxed themes (size, shape etc)  if you will, therefore making a hypotheses about how they inter-react is hard. Therefore forming a subset of models to test is very difficult, my approach has been to use all combinations of factors to generate the candidate models. I am worried that this approach is taking me down the data dredging/ model simplification route i am trying to avoid. Is it bad practice to use all combinations? As long as i rank them by akaike weight and use model averaging techniques isn't this OK?
>    

    If you are *really* trying to predict (rather than test hypotheses), 
and you really use model averaging, then I would be fine with this 
approach -- but then you wouldn't be spending any time worrying about 
which models were weighted how strongly (although I do admit that 
wondering why p-values and AIC gave different rankings is worth thinking 
about -- I'm just not sure there's a short answer without looking 
through all of the data).

   You should take a look at the AICcmodavg and MuMIn packages on CRAN 
-- one or the other may (?) be able to handle lmer fits.
>    
>> My best guess as to what's going on here is that you have a good deal of correlation among your factors
>>      
> I tested this with Pearson's R and only one combination showed up as having a strong correlation, is this not sufficient?
>    

     Often but not necessarily.  Zuur et al have a recent paper in 
Methods in Ecology and Evolution you might want to look at.
>    
>> some combinations of factors are under/overrepresented in the data set)
>>      
> Thats is certainly the case, but i cant do much about that, is it not just sufficent to rely on Pearson's values as mentioned above?
>
>    
>> simply fit
>> the full model and base your inference on the estimates and confidence intervals from the full mode
>>      
> I want to be able to predict the threat status ( the response variable) for species i only have traits (factors) for, this approach would not really let me do this would it?
>    

    I don't quite understand.

   Ben