[R-sig-ME] Using anova vs. Anova for linear mixed model

[Kevin Chu]

Hello,

I built a linear mixed effects model with three fixed factors and one random factor. I want to test for statistical significance of the fixed effects using F-tests from a type III ANOVA table. Since I am using a type III ANOVA, I understand that I need to set the contrasts to contr.sum so that the sums of squares are calculated correctly.

These are the data types.

> str(mydata)
'data.frame': 280 obs. of  5 variables:
 $ SUBJECT  : Factor w/ 20 levels "1","2","3","4",..: 1 1 1 1 1 1 1 1 1 1 ...
 $ CONDITION: Factor w/ 4 levels "anechoic","aula",..: 1 1 1 1 2 2 2 2 3 3 ...
 $ CHANNEL  : Factor w/ 2 levels "0","1": 1 1 2 2 1 1 2 2 1 1 ...
 $ STRATEGY : Factor w/ 2 levels "0","1": 1 2 1 2 1 2 1 2 1 2 ...
 $ SCORE    : num  107.4 57 90.1 96.1 -16.4 ...

Below is the code I used to generated the model.

lmm <- lmer(SCORE ~ CONDITION * CHANNEL * STRATEGY + (1 | SUBJECT), data=mydata, contrasts=list(CONDITION=contr.sum, CHANNEL=contr.sum, STRATEGY=contr.sum))

I tried passing lmm through anova from the stats package and Anova from the car package, but I obtained different results (screenshots are attached).

My questions:
1) Why do anova and Anova give different results even though I specified type III ANOVA?
2) Why is the Sum Sq equal to 0 in the table produced by anova?

I would prefer not to release the data as I plan to publish a paper based on my results, but if it helps I can create dummy data.

Thank you,
Kevin Chu

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