modelAvg {cAIC4} R Documentation

## Model Averaging for Linear Mixed Models

### Description

Function to perform Model Averaging for Linear Mixed Models based on the weight selection criterion Model Averaging as proposed by Zhang et al.(2014)

### Usage

```modelAvg(models, opt = TRUE)
```

### Arguments

 `models` A list object containing all considered candidate models fitted by `lmer` of the lme4-package or of class `lme`. `opt` logical. If TRUE (the default) the model averaging approach with optimial weights is calculated. If FALSE the underlying weights as smoothed weights as proposed by Buckland et al. (1997)

### Value

An object containing the function calls of the underlying candidate models, the values of the model averaged fixed effects, the values of the model averaged random effects the results of the weight optimization process, as well as a list of the candidate models themselvs

### 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.

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

### 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)
foo

```

[Package cAIC4 version 0.9 Index]