# How to obtain the overall p-values for the main effects and interactions in a glmer model?

I use glmer to model my data:

m1<-glmer(ambulation~ trt + test + trt:test
+(1|calf),
data= data)
summary(m1)


ambulation is a count variable (integer), trt, test, and calf are factors. I get:

Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']
Family: poisson  ( log )
Formula: ambulation ~ trt + test + trt:test + (1 | calf)
Data: data

AIC      BIC   logLik deviance df.resid
2525.1   2541.1  -1255.6   2511.1       65

Scaled residuals:
Min       1Q   Median       3Q      Max
-12.0392  -3.6718   0.1672   3.5336  14.2978

Random effects:
Groups Name        Variance Std.Dev.
calf   (Intercept)  0.2746   0.524
Number of obs: 72, groups:  calf, 24

Fixed effects:
Estimate Std. Error z value Pr(>|z|)
(Intercept)       4.78214    0.15344  31.166  < 2e-16 ***
trtmother         0.13612    0.21685   0.628   0.5302
testNO            0.13540    0.03282   4.126 3.69e-05 ***
testOF            0.36235    0.03122  11.605  < 2e-16 ***
trtmother:testNO -0.11084    0.04710  -2.353   0.0186 *
trtmother:testOF -0.32936    0.04595  -7.167 7.64e-13 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
(Intr) trtmth testNO testOF trt:NO
trtmother   -0.708
testNO      -0.114  0.081
testOF      -0.120  0.085  0.561
trtmthr:tNO  0.080 -0.113 -0.697 -0.391
trtmthr:tOF  0.082 -0.116 -0.381 -0.679  0.533


If I run anova(m1) instead of summary(m1) I get:

> anova(m1)
Analysis of Variance Table
Df Sum Sq Mean Sq F value
trt       1  0.024   0.024  0.0238
test      2 89.561  44.780 44.7805
trt:test  2 54.305  27.153 27.1525
>


How to get a report like anova(m1) but with associated p-values?

• Have you tried using the Anova() function from the car package instead of the anova() function from base R? See also stats.stackexchange.com/questions/223626/…, which suggests that anova(model, test="Chisq") should produce a p-value. Apr 1, 2019 at 15:27
• I edited your post by adding 4 white spaces in front of each line of R output. In the future, you can use this trick yourself to make sure your R output is readable. Apr 1, 2019 at 15:50