cAIC4 (version 1.0)

predictMA: 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.

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

# NOT RUN {
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)


# }