Timeline for Model selection in PGLS?
Current License: CC BY-SA 3.0
7 events
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| Apr 13, 2017 at 12:44 | history | edited | CommunityBot |
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| May 19, 2016 at 16:20 | comment | added | SlowLoris |
If your concern is reporting a ridiculous number of results, then you can choose to only report models above a certain Akaike weight. If your concern is doing everything by hand, then you might find help from an R package such as MuMln. You can also look into model averaging rather than model selection.
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| May 19, 2016 at 7:51 | comment | added | AlexR | Oh ok, but reporting the p-values for all the parameters/models is a bit tedious if you are modelling many parameter combinations? "spurious" in reference to the parameters excluded in best model determined by AIC. | |
| May 16, 2016 at 15:26 | comment | added | SlowLoris | Well what I was suggesting was to provide the p-values for all of the models/parameters, as well as the AIC. That way people can see how p-values change depending on the model specification. What do you mean "spurious parameters"? | |
| May 16, 2016 at 6:13 | comment | added | AlexR | Thanks for the clear answer. So should i use anova() for the model with all parameters or the best model selected by AIC? (I notice the output changes with spurious parameters). Cheers | |
| May 16, 2016 at 6:06 | vote | accept | AlexR | ||
| May 15, 2016 at 15:50 | history | answered | SlowLoris | CC BY-SA 3.0 |