umxWeightedAIC: AIC weight-based conditional probabilities.

In tbates/umx: Structural Equation Modeling and Twin Modeling in R

AIC weight-based conditional probabilities.

Description

Returns the best model by AIC, and computes the probabilities according to AIC weight-based conditional probabilities (Wagenmakers & Farrell, 2004).

Usage

umxWeightedAIC(models, digits = 2)

Arguments

models	a list of models to compare.
digits	(default 2)

Value

• Best model

References

• 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/

See Also

• AIC()

Other Miscellaneous Stats Functions: FishersMethod(), SE_from_p(), geometric_mean(), harmonic_mean(), oddsratio(), reliability(), umx, umxCov2cor(), umxHetCor(), umxParan(), umx_apply(), umx_cor(), umx_means(), umx_r_test(), umx_round(), umx_scale(), umx_var()

Examples

l1 = lm(mpg~ wt + disp, data=mtcars)
l2 = lm(mpg~ wt, data=mtcars)
umxWeightedAIC(models = list(l1, l2))

tbates/umx documentation built on Dec. 14, 2024, 11:28 a.m.