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How to compare two mixed effects in R?
I wish to use AIC to compare two mixed effects models generated using the lme4 package in R. Each model has one fixed effect and one random effect. The fixed effect differs between models, but the random effect remains the same between models.
How to compare two linear mixed effect models?
Both models must use the same response vector in the fit and lme must be nested in altlme for a valid theoretical likelihood ratio test. Always input the smaller model first, and the larger model second. compare tests the following null and alternate hypotheses:
How to name a model in mixed effects?
You can name each model whatever you want, but note that the name of the dataframe containing your data is specified in each model. Keep REML = FALSE. First, however, we need to specify the random effects term that best fits the data.
How is ANOVA used in mixed effects modeling?
The ANOVA function allows you to compute Chi-squares between each model to see the improvement in model fit. The effects package should also include p-values in the output.
What is the difference between ML and REML?
However, I notice that when I use ML, the residual variance associated with the random effects differs between the two models ( model1 = 136.3; model2 = 112.9), but when I use REML, it is the same between models (model1=model2=151.5).
How does REML affect the linear mixed model?
Let us check how this additional term coming from REML affects the solution of the Linear Mixed Model (LMM) for the toy data set that was introduced in the LMM from Scratch post. To recap, we were considering only 4 data points for simplicity: 2 originating from Individual #1 and the other 2 coming from Individual #2.
Can you use REML to compare two models?
Zuur et al., and Faraway (from @janhove’s comment above) are right; using likelihood-based methods (including AIC) to compare two models with different fixed effects that are fitted by REML will generally lead to nonsense.