predictMA: Prediction of model averaged linear mixed models

In cAIC4: Conditional Akaike Information Criterion for 'lme4' and 'nlme'

Prediction of model averaged linear mixed models

Description

Function to perform prediction for model averaged linear mixed models based on the weight selection criterion as proposed by Zhang et al.(2014)

Usage

predictMA(object, new.data)

Arguments

object	A object created by the model averaging function.
new.data	Object that contains the data on which the prediction is to be based on.

Value

An object that contains predictions calculated based on the given dataset and the assumed underlying model average.

Author(s)

Benjamin Saefken & Rene-Marcel Kruse

References

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

See Also

lme4-package, lmer

Examples

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 <- modelAvg(models = models)
predictMA(foo, new.data = Orthodont)

cAIC4 documentation built on April 11, 2025, 6:10 p.m.