getWeights: Optimize weights for model averaging.
In cAIC4: Conditional Akaike Information Criterion for 'lme4' and 'nlme'

Description Usage Arguments Value WARNINGS Author(s) References See Also Examples

Function to determine optimal weights for model averaging based on a proposal by Zhang et al. ( 2014) to derive a weight choice criterion based on the conditional Akaike Information Criterion as proposed by Greven and Kneib (2010). The underlying optimization is a customized version of the Augmented Lagrangian Method.

1	getWeights(models)

models

An list object containing all considered candidate models fitted by lmer of the lme4-package or of class lme.

An object containing a vector of optimized weights, value of the minimized target function and the duration of the optimization process.

No weight-determination is currently possible for models called via gamm4.

Benjamin Saefken & Rene-Marcel Kruse

Greven, S. and Kneib T. (2010) On the behaviour of marginal and conditional AIC in linear mixed models. Biometrika 97(4), 773-789.

Zhang, X., Zou, G., & Liang, H. (2014). Model averaging and weight choice in linear mixed-effects models. Biometrika, 101(1), 205-218.

Nocedal, J., & Wright, S. (2006). Numerical optimization. Springer Science & Business Media.

lme4-package, lmer, getME

data(Orthodont, package = "nlme")
models <- list(
    model1 <- lmer(formula = distance ~ age + Sex + (1 | Subject) + age:Sex,
               data = Orthodont),
    model2 <- lmer(formula = distance ~ age + Sex + (1 | Subject),
               data = Orthodont),
    model3 <- lmer(formula = distance ~ age + (1 | Subject),
                 data = Orthodont),
    model4 <- lmer(formula = distance ~ Sex + (1 | Subject),
                data = Orthodont))

foo <- getWeights(models = models)
foo