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tips:model_selection_with_glmulti_and_mumin

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tips:model_selection_with_glmulti_and_mumin [2022/09/25 10:45] Wolfgang Viechtbauertips: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="d", fitfunction=rma.glmulti, crit="aicc")+        level=2, marginality=TRUE, method="d", fitfunction=rma.glmulti, crit="aicc")
 </code> </code>
 <code output> <code output>
-Initialization...+Initialization...                                                                                                       
 TASK: Diagnostic of candidate set. TASK: Diagnostic of candidate set.
 Sample size: 41 Sample size: 41
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 Size constraints: min =  0 max = -1 Size constraints: min =  0 max = -1
 Complexity constraints: min =  0 max = -1 Complexity constraints: min =  0 max = -1
-Your candidate set contains 268435456 models. +Marginality rule. 
-[1] 268435456+Your candidate set contains 2350602 models. 
 +[1] 2350602
 </code> </code>
  
-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 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.
  
 ==== 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