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rmodelmumin

Can I use AICc to rank models based in nested data?

I have a database where there are 136 species for 6 variables. For 4 variables there are data for all species. However, for the other 2 variables, there are data for only 88 species. When we look at the 6 variables together, only 78 species have data for all variables.

So, i ran models using this variables.

Note that the models have different species sample sizes, varying according to the data in the database note that the models have different species sample sizes, varying according to the data in the database

I need to know if AICc is a good way to compare these models.

The model.avg in MuMIn package returns a error when i try to run a list including all my models:

mods <- list(mod1, mod2, ..., mod14)
aicc <- summary(model.avg(mods))

*Error in model.avg.default(mods) : 
models are not all fitted to the same data*

This error makes me think that is not possible rank models based in different sample sizes using AICc. I need help to solve this question!

Thanks in advance!

Solution

  • Basically, all information criteria (as AIC is) are based on the likelihood function of the model that is influenced by sample size. The sample size is directly correlated with information criteria (greater sample size = lower likelihood = greater information criteria). This means that you cannot compare different sample-size models using AIC or any information criteria.

    That's also why your model.avg is failing.