Model.Fit {CorrMixed} R Documentation

## Compare the fit of linear mixed-effects models

### Description

This function compares the fit of Model 1 (random intercept) and 2 (random intercept and Gausssian serial correlation), and of Model 2 (random intercept and Gausssian serial correlation) and 3 (random intercept, slope and Gausssian serial correlation)

### Usage

Model.Fit(Model.1, Model.2)


### Arguments

 Model.1 An object of class WS.Corr.Mixed, the first fitted model. Model.2 Another object of class WS.Corr.Mixed, the second fitted model.

### Author(s)

Wim Van der Elst, Geert Molenberghs, Ralf-Dieter Hilgers, & Nicole Heussen

### References

Van der Elst, W., Molenberghs, G., Hilgers, R., & Heussen, N. (2015). Estimating the reliability of repeatedly measured endpoints based on linear mixed-effects models. A tutorial. Submitted.

WS.Corr.Mixed

### Examples

data(Example.Data)

# Code predictors for time
Example.Data$Time2 <- Example.Data$Time**2
Example.Data$Time3 <- Example.Data$Time**3
Example.Data$Time3_log <- (Example.Data$Time**3) * (log(Example.Data\$Time))

# model 1
Model1 <- WS.Corr.Mixed(
Fixed.Part=Outcome ~ Time2 + Time3 + Time3_log + as.factor(Cycle)
+ as.factor(Condition), Random.Part = ~ 1|Id,
Dataset=Example.Data, Model=1, Id="Id",
Number.Bootstrap = 0, Seed = 12345)

# model 2
Model2 <- WS.Corr.Mixed(
Fixed.Part=Outcome ~ Time2 + Time3 + Time3_log + as.factor(Cycle)
+ as.factor(Condition), Random.Part = ~ 1|Id,
Correlation=corGaus(form= ~ Time, nugget = TRUE),
Dataset=Example.Data, Model=2, Id="Id",
Number.Bootstrap = 0, Seed = 12345)

# model 3
Model3 <- WS.Corr.Mixed(
Fixed.Part=Outcome ~ Time2 + Time3 + Time3_log + as.factor(Cycle)
+ as.factor(Condition), Random.Part = ~ 1 + Time|Id,
Correlation=corGaus(form= ~ Time, nugget = TRUE),
Dataset=Example.Data, Model=3, Id="Id",
Number.Bootstrap = 0, Seed = 12345)

# compare models 1 and 2
Model.Fit(Model.1=Model1, Model.2=Model2)

# compare models 2 and 3
Model.Fit(Model.1=Model2, Model.2=Model3)


[Package CorrMixed version 1.1 Index]