tips:model_selection_with_glmulti_and_mumin
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tips:model_selection_with_glmulti_and_mumin [2022/09/25 10:45] – Wolfgang Viechtbauer | tips:model_selection_with_glmulti_and_mumin [2022/10/13 06:07] (current) – Wolfgang Viechtbauer | ||
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<code rsplus> | <code rsplus> | ||
glmulti(yi ~ length + wic + feedback + info + pers + imag + meta, data=dat, | glmulti(yi ~ length + wic + feedback + info + pers + imag + meta, data=dat, | ||
- | level=2, method=" | + | level=2, marginality=TRUE, method=" |
</ | </ | ||
<code output> | <code output> | ||
- | Initialization... | + | Initialization... |
TASK: Diagnostic of candidate set. | TASK: Diagnostic of candidate set. | ||
Sample size: 41 | Sample size: 41 | ||
Line 275: | Line 275: | ||
Size constraints: | Size constraints: | ||
Complexity constraints: | Complexity constraints: | ||
- | Your candidate set contains | + | Marginality rule. |
- | [1] 268435456 | + | Your candidate set contains |
+ | [1] 2350602 | ||
</ | </ | ||
- | So, the candidate set would include over $2 \times 10^8$ possible models. Fitting all of these models would not only test our patience (and would be a waste of valuable CPU cycles), it would also be a pointless exercise (even fitting the 128 models above could be critiqued by some as a mindless hunting expedition -- although if one does not get too fixated on //the// best model, but considers all the models in the set as part of a multimodel inference approach, this critique loses some of its force). So, I won't consider this any further in this example. | + | So, the candidate set would include over two million |
==== Using the MuMIn Package ==== | ==== Using the MuMIn Package ==== |
tips/model_selection_with_glmulti_and_mumin.txt · Last modified: 2022/10/13 06:07 by Wolfgang Viechtbauer