AIC weight-based conditional probabilities.
Returns the best model by AIC, and computes the probabilities according to AIC weight-based conditional probabilities (Wagenmakers & Farrell, 2004).
umxWeightedAIC(models, digits = 2)
models |
a list of models to compare. |
digits |
(default 2) |
Best model
Wagenmakers E.J., Farrell S. (2004), 192-196. AIC model selection using Akaike weights. Psychonomic Bulletin and Review. 11, 192-196. https://pubmed.ncbi.nlm.nih.gov/15117008/
Other Miscellaneous Stats Functions:
FishersMethod(),
SE_from_p(),
geometric_mean(),
harmonic_mean(),
oddsratio(),
reliability(),
umxCov2cor(),
umxHetCor(),
umx_apply(),
umx_cor(),
umx_means(),
umx_r_test(),
umx_round(),
umx_scale(),
umx_var(),
umx
l1 = lm(mpg~ wt + disp, data=mtcars) l2 = lm(mpg~ wt, data=mtcars) umxWeightedAIC(models = list(l1, l2))
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