Model.Fit | R Documentation |
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)
Model.Fit(Model.1, Model.2)
Model.1 |
An object of class |
Model.2 |
Another object of class |
Wim Van der Elst, Geert Molenberghs, Ralf-Dieter Hilgers, & Nicole Heussen
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
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)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.