I want to use model comparison to estimate the significance the effects used in a linear mixed effects model (using R's
In theory, I could create multiple nested models, each model removing only one factor (either a main effect or an interaction), and comparing in to the full model using R's
anova(). However, since I have too many such models to create, I decided to use
Anova() (Type III), because as far as my understanding goes it should be equal to all the model comparisons I intended to run "manually". However, I get different results. For instance, the result of one such model comparison, where
model_final_full includes all main effects and interaction, and
model_nested_Polarity is identical but removing only one factor (main effect of Polarity):
The Chi-square and p-value is very different comapred to when running Anova():
anova() I get Chi-square=52.754 and p=3.782e-13. With
Anova() I get for the Polarity factor Chi-square=116.3031 and p< 2.2e-16.
What am I missing? And what is the right way of doing it then?
Many thanks in advance.
I found the function
mixed() (in the
afex package) that does exactly what I was hoping
Anova() would do - it does automatically the model comparisons of the full model with all the possible nested models.
I still don't know what Anova() does and why it is different.