# running glmer with factors that have more than 2 levels?

I am trying to run the glmer() function on results from an experiment I conducted. The dependent variable, FirstResponseKey, is a binomial variable (0\1). The model looks like this:

mflicker <- glmer(FirstResponseAccuracy ~ 1 + Aware*lag*SameLocation + (1|Subject),
family = binomial(link="logit"), data = data_flicker)
summary(mflicker)
meansFlicker <- effect("Aware*lag*SameLocation", mflicker)
summary(meansFlicker)


All fixed variables are factors, Aware and SameLocation have 2 levels, while leg have 3 level- which created unexpected problems. The problem is that the output gives me p-values for each level of the factor lag separately, like this:

    Fixed effects:
Estimate Std. Error z value Pr(>|z|)
(Intercept)                0.7143714  0.2636160   2.710  0.00673 **
Aware1                    -0.4173583  0.1577041  -2.647  0.00813 **
lag3                      -0.0685717  0.1802532  -0.380  0.70363
lag7                      -0.3193594  0.1799608  -1.775  0.07596 .
SameLocation1              0.0937725  0.1797897   0.522  0.60197
Aware1:lag3                0.0165164  0.2148904   0.077  0.93874
Aware1:lag7                0.6276651  0.2154301   2.914  0.00357 **
Aware1:SameLocation1       0.3485323  0.2170429   1.606  0.10831
lag3:SameLocation1        -0.1404866  0.2581919  -0.544  0.58636
lag7:SameLocation1        -0.0004234  0.2615045  -0.002  0.99871
Aware1:lag3:SameLocation1 -0.5017417  0.3080803  -1.629  0.10340
Aware1:lag7:SameLocation1 -0.4578527  0.3115991  -1.469  0.14173


I don't know how to interpret these results. I want the main effect and interactions of lag as a whole, not by levels.

I tried scaling and centering the lag factor, and then the model indeed gives me the effect of lag across levels - but then I have a different problem: when I try to print out the mean of each condition, I get 5 different levels of lag although there are supposed to be only 3!

Theoretically, I can regard lag both as a factor or as an ordinal variable - but I can't make either of these options work.

What should I do?

Edit- Here is a reproducible example.The remaining issue is 1) I don't know how to perform pairwise comparisons between every two simple conditions. I tried to follow other posts but it didnt work with 3 ID variables. 2)Even though I get many significant effects, the SE are extremely high- about 5 times higher compared to when I use LME instead of GLM. (see my question in this post: strangely large se when using glm compared to lme)

      data_Report <-  structure(list(lag = structure(c(1L, 2L, 3L, 3L, 2L, 2L,
2L, 3L, 2L, 2L, 2L, 2L, 2L, 3L, 2L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 3L,
3L, 1L, 3L, 1L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 3L, 2L, 3L,
3L, 3L, 2L, 1L, 2L, 1L, 2L, 3L, 2L, 2L, 3L, 2L, 3L, 3L, 1L, 3L,
3L, 1L, 3L, 1L, 2L, 1L, 2L, 3L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 3L,
3L, 3L, 2L, 2L, 1L, 2L, 1L, 1L, 3L, 3L, 3L, 2L, 3L, 1L, 1L, 3L,
3L, 3L, 3L, 3L, 2L, 3L, 3L, 2L, 1L, 2L, 3L, 2L, 1L, 2L, 2L, 3L,
3L, 1L, 1L, 2L, 2L, 2L, 3L, 2L, 2L, 1L, 3L, 3L, 3L, 1L, 1L, 1L,
1L, 1L, 2L, 3L, 1L, 2L, 2L, 1L, 3L, 2L, 2L, 3L, 3L, 3L, 2L, 2L,
2L, 2L, 1L, 2L, 3L, 2L, 3L, 2L, 3L, 1L, 3L, 1L, 3L, 3L, 3L, 2L,
1L, 2L, 2L, 2L, 3L, 2L, 1L, 1L, 3L, 3L, 3L, 1L, 3L, 1L, 1L, 1L,
2L, 3L, 2L, 2L, 1L, 1L, 2L, 3L, 2L, 1L, 1L, 2L, 1L, 1L, 3L, 3L,
1L, 3L, 3L, 2L, 2L, 1L, 3L, 2L, 3L, 1L, 3L, 2L, 2L, 1L, 2L, 1L,
1L, 2L, 2L, 2L, 3L, 1L, 2L, 2L, 2L, 2L, 3L, 1L, 1L, 3L, 3L, 2L,
3L, 1L, 2L, 2L, 3L, 2L, 1L, 3L, 2L, 2L, 3L, 1L, 1L, 3L, 1L, 1L,
2L, 2L, 2L, 1L, 2L, 3L, 1L, 3L, 3L, 1L, 2L, 3L, 2L, 2L, 3L, 1L,
2L, 1L, 3L, 2L, 3L, 2L, 2L, 1L, 1L, 3L, 1L, 1L, 2L, 2L, 2L, 2L,
2L, 3L, 2L, 2L, 2L, 2L, 3L, 2L, 2L, 1L, 3L, 2L, 1L, 2L, 1L, 2L,
2L, 1L, 2L, 2L, 1L, 1L, 1L, 3L, 3L, 1L, 3L, 3L, 2L, 3L, 1L, 1L,
2L, 1L, 3L, 3L, 3L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 3L, 3L, 2L,
2L, 3L, 2L, 3L, 2L, 1L, 3L, 1L, 2L, 3L, 1L, 2L, 1L, 2L, 3L, 1L,
2L, 2L, 1L, 3L, 2L, 2L, 2L, 1L, 1L, 3L, 2L, 1L, 1L, 2L, 3L, 3L,
1L, 2L, 1L, 1L, 2L, 2L, 1L, 3L, 1L, 2L, 2L, 3L, 3L, 1L, 1L, 2L,
3L, 1L, 3L, 3L, 1L, 3L, 1L, 1L, 3L, 1L, 2L, 1L, 3L, 2L, 3L, 2L,
1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 3L, 2L, 3L, 3L, 3L, 1L, 3L, 2L,
1L, 1L, 1L, 1L, 2L, 2L, 1L, 3L, 3L, 3L, 2L, 2L, 3L, 1L, 2L, 3L,
1L, 1L, 3L, 3L, 1L, 1L, 3L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 3L,
3L, 2L, 3L, 3L, 1L, 2L, 1L, 2L, 3L, 3L, 2L, 1L, 1L, 3L, 3L, 2L,
2L, 1L, 2L, 3L, 3L, 1L, 1L, 2L, 2L, 2L, 1L, 3L, 2L, 1L, 3L, 3L,
1L, 2L, 2L, 1L, 2L, 2L, 3L, 2L, 1L, 1L, 1L, 3L, 1L, 2L, 2L, 2L,
1L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 3L, 2L, 2L, 3L, 1L, 1L, 2L, 2L,
3L, 3L, 2L, 1L, 3L, 1L, 3L, 3L, 3L, 3L, 3L, 1L, 2L, 3L, 2L, 1L,
1L, 1L, 2L, 3L, 2L, 3L, 1L, 1L, 3L, 2L, 1L, 3L, 3L, 3L, 3L, 2L,
3L, 3L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 3L, 2L, 1L, 3L, 1L, 1L, 3L,
1L, 2L, 2L, 3L, 1L, 1L, 1L, 3L, 3L, 3L, 3L, 1L, 3L, 1L, 1L, 2L,
2L, 1L, 1L, 1L, 3L, 2L, 3L, 2L, 2L, 1L, 2L, 3L, 3L, 3L, 2L, 2L,
2L, 3L, 2L, 2L, 1L, 2L, 2L, 2L, 3L, 2L, 1L, 2L, 1L, 3L, 3L, 2L,
3L, 1L, 1L, 3L, 3L, 1L, 3L, 1L, 2L, 2L, 3L, 3L, 1L, 2L, 1L, 3L,
2L, 3L, 1L, 2L, 2L, 3L, 3L, 2L, 3L, 1L, 1L, 3L, 3L, 3L, 2L, 1L,
2L, 3L, 2L, 1L, 3L, 2L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 1L, 1L,
2L, 1L, 3L, 2L, 3L, 3L, 3L, 2L, 2L, 3L, 3L, 1L, 2L, 3L, 1L, 3L,
1L, 1L, 2L, 3L, 3L, 1L, 3L, 2L, 3L, 3L, 2L, 1L, 2L, 2L, 3L, 2L,
3L, 2L, 1L, 2L, 1L, 3L, 1L, 2L, 3L, 3L, 1L, 3L, 2L, 1L, 3L, 3L,
1L, 1L, 3L, 1L, 3L, 1L, 3L, 2L, 1L, 1L, 3L, 2L, 3L, 3L, 3L, 2L,
3L, 1L, 1L, 2L, 3L, 1L, 3L, 1L, 2L, 3L, 1L, 3L, 3L, 3L, 1L, 1L,
3L, 1L, 2L, 1L, 3L, 2L, 3L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 1L, 2L,
3L, 1L, 1L, 1L, 3L, 2L, 2L, 2L, 2L, 2L, 3L, 1L, 1L, 3L, 1L, 3L,
3L, 1L, 1L, 3L, 3L, 1L, 1L, 2L, 1L, 2L, 3L, 1L, 3L, 3L, 2L, 2L,
2L, 1L, 3L, 3L, 2L, 3L, 3L, 1L, 1L, 3L, 1L, 2L, 2L, 2L, 1L, 3L,
3L, 1L, 2L, 3L, 1L, 2L, 1L, 1L, 3L, 3L, 1L, 2L, 1L, 3L, 3L, 1L,
1L, 1L, 2L), .Label = c("1", "3", "7"), class = "factor"), Aware =           structure(c(2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L,
2L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L,
1L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 1L, 1L,
1L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 2L, 1L, 1L, 2L,
2L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L,
2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 1L,
1L, 2L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L,
2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 2L,
2L, 1L, 2L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L,
2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 1L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
1L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 1L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L,
2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 2L,
1L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 1L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L,
2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 1L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L,
1L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 2L,
2L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L), .Label = c("0", "1"), class = "factor"),
SameLocation = structure(c(2L, 2L, 2L, 1L, 1L, 2L, 1L, 1L,
2L, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 2L,
2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 2L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 1L, 2L,
1L, 2L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L,
2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 1L, 1L, 2L, 2L,
1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 1L,
1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 1L,
2L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 2L,
1L, 2L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 2L,
2L, 2L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 2L,
2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 2L, 1L, 1L,
2L, 2L, 2L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 2L,
1L, 2L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 2L,
1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 2L, 2L,
2L, 2L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 1L,
2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 1L,
1L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 1L, 2L,
2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 1L, 2L,
1L, 2L, 1L, 1L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 1L,
1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L,
1L, 2L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 2L, 1L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 2L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L,
2L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L,
2L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 1L, 1L,
2L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 2L, 1L,
1L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L,
2L, 2L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L,
2L, 2L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 2L,
1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L,
2L, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 2L,
2L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 1L,
2L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 2L,
2L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 1L,
1L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 1L, 2L, 1L, 2L, 1L, 1L, 1L,
1L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L,
2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 2L,
1L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 1L,
1L, 2L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 2L,
2L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 2L,
1L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L,
1L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 2L, 1L,
1L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 1L,
2L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 2L,
2L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 2L, 1L, 2L, 1L,
2L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 1L, 2L, 1L, 1L,
1L, 2L, 2L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 2L,
2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 2L, 2L, 1L,
2L, 1L, 1L, 2L, 1L, 2L), .Label = c("0", "1"), class = "factor"),
Subject = c(102L, 102L, 102L, 102L, 102L, 102L, 102L, 102L,
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106L, 106L, 106L, 108L, 108L, 108L, 108L, 108L, 108L, 108L,
108L, 108L, 108L, 108L, 108L, 108L, 108L, 108L, 108L, 108L,
108L, 108L, 108L, 108L, 108L, 108L, 108L, 108L, 108L, 108L,
108L, 108L, 108L, 108L, 108L, 108L, 108L, 108L, 108L, 108L,
108L, 108L, 108L, 108L, 108L, 108L, 108L, 108L, 108L, 108L,
108L, 108L, 108L, 108L, 108L, 108L, 108L, 108L, 108L, 108L,
108L, 108L, 108L, 108L, 108L, 108L, 108L, 108L, 108L, 108L,
108L, 108L, 108L, 108L, 108L, 108L, 108L, 108L, 108L, 108L,
108L, 108L, 108L, 108L, 108L, 108L, 108L, 108L, 108L, 108L,
108L, 108L, 108L, 108L, 108L, 108L, 108L, 108L, 108L, 108L,
108L, 108L, 108L, 108L, 108L, 108L, 108L, 108L, 108L, 108L,
108L, 108L, 108L, 108L, 108L, 108L, 108L, 108L, 108L, 108L,
108L, 108L, 108L, 108L, 108L, 108L, 108L, 108L, 108L, 108L,
108L, 108L, 108L, 108L, 108L, 108L, 108L, 108L, 108L, 108L,
108L, 108L, 108L, 108L, 108L, 115L, 115L, 115L, 115L, 115L,
115L, 115L, 115L, 115L, 115L, 115L, 115L, 115L, 115L, 115L,
115L, 115L, 115L, 115L, 115L, 115L, 115L, 115L, 115L, 115L,
115L, 115L, 115L, 115L, 115L, 115L, 115L, 115L, 115L, 115L,
115L, 115L, 115L, 115L, 115L, 115L, 115L, 115L, 115L, 115L,
115L, 115L, 115L, 115L, 115L, 115L, 115L, 115L, 115L, 115L,
115L, 115L, 115L, 115L, 115L, 115L, 115L, 115L, 115L, 115L,
115L, 115L, 115L, 115L, 115L, 115L, 115L, 115L, 115L, 115L,
115L, 115L, 115L, 115L, 115L, 115L, 115L, 115L, 115L, 115L,
115L, 115L, 115L, 115L, 115L, 115L, 115L, 115L, 115L, 115L,
115L, 115L, 115L, 115L, 115L, 115L, 115L, 115L, 115L, 115L,
115L, 115L, 115L, 115L, 115L, 115L, 115L, 115L, 115L, 115L,
115L, 115L, 115L, 115L, 115L, 115L, 115L, 115L, 115L, 115L,
115L, 115L, 115L, 115L, 115L, 115L, 115L, 115L, 115L, 115L,
115L, 115L, 118L, 118L, 118L, 118L, 118L, 118L, 118L, 118L,
118L, 118L, 118L, 118L, 118L, 118L, 118L, 118L, 118L, 118L,
118L, 118L, 118L, 118L, 118L, 118L, 118L, 118L, 118L, 118L,
118L, 118L, 118L, 118L, 118L, 118L, 118L, 118L, 118L, 118L,
118L, 118L, 118L, 118L, 118L, 118L, 118L, 118L, 118L, 118L,
118L, 118L, 118L, 118L, 118L, 118L, 118L, 118L, 118L, 118L,
118L, 118L, 118L, 118L, 118L, 118L, 118L, 118L, 118L, 118L,
118L, 118L, 118L, 118L, 118L, 118L, 118L, 118L, 118L, 118L,
118L, 118L, 118L, 118L, 118L, 118L, 118L, 118L, 118L, 118L,
118L, 118L, 118L, 118L, 118L, 118L, 118L, 118L, 118L, 118L,
118L, 118L, 118L, 118L, 118L, 118L, 118L, 118L, 118L, 118L,
118L, 118L, 118L, 118L, 118L, 118L), FirstResponseAccuracy = c(1L,
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1L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 0L, 0L, 0L,
1L, 1L, 1L, 1L, 0L, 1L, 0L, 0L, 1L, 0L, 1L, 1L, 1L, 1L, 0L,
1L, 1L, 0L, 0L, 1L, 0L, 0L, 1L, 1L, 0L, 1L, 1L, 1L, 0L, 1L,
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1L, 1L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 1L, 0L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 1L,
1L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 1L, 0L, 0L, 1L, 1L, 0L,
0L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L,
1L, 0L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 0L, 1L, 0L, 1L, 1L, 1L,
1L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 0L, 0L,
0L, 0L, 0L, 0L, 1L, 0L, 1L, 0L, 1L, 1L, 1L, 1L, 0L, 1L, 1L,
0L, 1L, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 0L, 0L,
0L, 0L, 1L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 1L, 1L, 1L, 0L, 1L,
0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 0L, 0L, 1L,
0L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 0L, 0L, 1L,
1L, 0L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 0L, 0L, 0L, 1L, 0L, 0L,
0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L,
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0L, 1L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
1L, 1L, 0L, 1L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 0L, 1L,
0L, 0L, 1L, 1L, 0L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L,
0L, 1L, 1L, 0L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L,
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1L, 1L, 0L, 1L, 1L, 0L, 1L, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 1L,
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0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 0L, 1L,
1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 0L, 1L, 1L, 1L, 0L)), .Names = c("lag",
"Aware", "SameLocation", "Subject", "FirstResponseAccuracy"), row.names = c(3L,
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104L, 118L, 128L, 130L, 132L, 135L, 136L, 143L, 148L, 152L, 153L,
156L, 161L, 164L, 165L, 169L, 172L, 183L, 185L, 186L, 188L, 190L,
199L, 200L, 203L, 206L, 207L, 213L, 216L, 217L, 218L, 220L, 225L,
229L, 243L, 245L, 246L, 247L, 249L, 250L, 252L, 253L, 255L, 258L,
259L, 261L, 262L, 265L, 266L, 268L, 270L, 271L, 279L, 281L, 282L,
292L, 298L, 299L, 303L, 305L, 308L, 311L, 312L, 315L, 319L, 321L,
327L, 333L, 335L, 337L, 339L, 340L, 342L, 348L, 349L, 350L, 354L,
371L, 373L, 374L, 375L, 376L, 380L, 383L, 384L, 387L, 389L, 390L,
392L, 396L, 398L, 400L, 406L, 408L, 415L, 419L, 421L, 422L, 426L,
431L, 437L, 443L, 444L, 446L, 449L, 454L, 460L, 471L, 477L, 478L,
484L, 485L, 493L, 494L, 501L, 502L, 503L, 505L, 506L, 507L, 512L,
514L, 525L, 528L, 530L, 532L, 536L, 543L, 544L, 551L, 552L, 561L,
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626L, 628L, 635L, 642L, 645L, 647L, 657L, 658L, 659L, 660L, 663L,
664L, 667L, 671L, 672L, 676L, 677L, 689L, 690L, 695L, 703L, 705L,
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735L, 737L, 738L, 741L, 742L, 749L, 752L, 754L, 755L, 757L, 758L,
762L, 763L, 773L, 777L, 784L, 787L, 800L, 810L, 812L, 819L, 820L,
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1936L, 1937L, 1948L, 1951L, 1952L, 1959L, 1968L, 1969L, 1974L,
1975L, 1976L, 1979L, 1985L, 1991L, 1994L, 1995L, 1996L, 1998L,
1999L, 2002L, 2010L, 2016L, 2018L, 2020L, 2028L, 2033L, 2043L,
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2143L, 2145L, 2156L, 2161L, 2166L, 2172L, 2173L, 2174L, 2175L,
2178L, 2187L, 2188L, 2193L, 2201L, 2207L, 2210L, 2218L, 2219L,
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2907L, 2913L, 2920L, 2921L, 2926L, 2929L, 2932L, 2933L, 2936L,
2937L, 2938L, 2941L, 2946L, 2947L, 2952L, 2953L, 2954L, 2957L,
2964L, 2966L, 2972L, 2975L, 2979L, 2984L, 2988L, 2989L, 2990L,
2992L, 2994L, 2997L, 3000L, 3001L, 3007L, 3015L, 3016L, 3017L,
3018L, 3019L, 3024L, 3031L, 3038L, 3040L, 3042L, 3049L, 3054L,
3055L, 3065L, 3071L, 3072L, 3075L, 3076L, 3082L, 3088L, 3090L,
3098L, 3105L, 3107L, 3108L, 3111L, 3117L, 3119L, 3121L, 3128L,
3130L, 3131L, 3136L, 3140L, 3142L, 3148L, 3149L, 3153L, 3156L,
3157L, 3159L, 3163L, 3166L, 3169L, 3171L, 3175L, 3179L, 3180L,
3182L, 3183L, 3185L, 3190L, 3196L, 3197L, 3200L, 3202L, 3203L,
3204L, 3208L, 3210L, 3212L, 3217L, 3221L, 3222L, 3223L, 3227L,
3231L, 3235L, 3239L, 3242L, 3245L, 3246L, 3247L, 3248L, 3249L,
3250L, 3252L, 3260L, 3276L, 3277L, 3278L, 3283L, 3285L, 3290L,
3291L, 3292L, 3294L, 3295L, 3297L, 3303L, 3308L, 3310L, 3315L,
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6246L, 6247L, 6249L, 6250L, 6259L, 6260L, 6262L, 6265L, 6266L,
6270L, 6274L, 6275L, 6278L, 6282L, 6283L, 6284L, 6286L, 6290L,
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6333L, 6334L, 6336L, 6339L, 6345L, 6351L, 6352L, 6365L, 6368L,
6369L, 6371L, 6375L, 6381L, 6382L, 6384L, 6386L, 6388L, 6393L,
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6422L, 6426L, 6431L, 6432L, 6436L, 6438L, 6439L, 6440L, 6456L,
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6554L, 6558L, 6559L, 6560L, 6562L, 6566L, 6569L, 6571L, 6573L,
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6618L, 6619L, 6621L, 6622L, 6628L, 6631L, 6636L, 6637L, 6640L,
6643L, 6644L, 6653L, 6668L, 6670L, 6678L, 7644L, 7650L, 7656L,
7657L, 7662L, 7664L, 7665L, 7666L, 7667L, 7670L, 7671L, 7676L,
7677L, 7680L, 7682L, 7683L, 7691L, 7692L, 7697L, 7707L, 7711L,
7713L, 7715L, 7717L, 7723L, 7725L, 7728L, 7731L, 7732L, 7736L,
7742L, 7749L, 7760L, 7762L, 7771L, 7772L, 7774L, 7775L, 7776L,
7777L, 7782L, 7794L, 7797L, 7801L, 7803L, 7808L, 7810L, 7812L,
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8060L, 8063L, 8066L, 8077L, 8079L, 8080L, 8092L, 8093L, 8098L,
8100L, 8107L, 8109L), class = "data.frame")

mm=glmer(FirstResponseAccuracy ~ 1 + lag + Aware + SameLocation+ lag:Aware + lag:SameLocation + Aware:SameLocation + lag:Aware:SameLocation + (1|Subject), family = binomial(link="logit"), data = data_Report)
#this summary function doesnt work well in glm when using variables with 3 levels..
summary(mm)
#this anova does not provide p values
anova(mm)
#this Anova provide p values
####install.packages("car")
library(car)
car::recode
Anova(mm)

#what is the mean and se in each condition? SE are ridiculously high!!(and much #much smaller when I use lme...)
NoRe <- effect("lag:Aware:SameLocation",se = TRUE, mm)
MeansNoRe <- as.data.frame(NoRe)
MeansNoRe

#pairwise comparisons???  

• could you try whether: <code>glmer(FirstResponseAccuracy ~ 1 + lag + Aware + SameLocation+ lag:Aware + lag:SameLocation + lag:Aware:SameLocation + (1|Subject), family = binomial(link="logit"), data = data_flicker)</code> gives you what you want?
– IWS
Commented Feb 10, 2017 at 12:28
• Would you be happy with the output of anova(mflicker)? Commented Feb 13, 2017 at 7:34
• The first part of your suggestion did not solve the problem in the output (still separate effects for each level of lag). The output of anova(mflicker) looks great but lacks p values.. Commented Feb 13, 2017 at 11:37
• Load the lmerTest package and try again. Commented Feb 15, 2017 at 11:32
• Here is a reproducible example: Commented Mar 6, 2017 at 11:10

To see an ANOVA-type table of the model fit, use the mixed() function of the afex package:

library(afex)
mixed(FirstResponseAccuracy ~ 1 + Aware*lag*SameLocation + (1|Subject),
family = binomial(link="logit"), data = data_flicker, method="PB")


Note: You have the specify the method how to calculate the p-values. In this case, I chose method="PB", which stands for parametric bootstrap. This may not be ideal in your scenario and it could take some time to compute. You need to read the Details section in the help file ?mixed.

To get the means, I would use the lsmeans() function from the lsmeans package, for example:

library(lsmeans)
lsmeans(mflicker, c("Aware","lag","SameLocation"))


You can also use the lsmeans() function to test for significant differences between the levels of your factors, for example:

lsmeans(mflicker, pairwise ~ Aware*lag*SameLocation)


Same here, have a look at the lsmeans documentation.

EDIT:

Using your reproducible example everything works fine as outlined above, except for the Model failed to converge warning. As per this thread here, add glmerControl(optimizer="bobyqa", optCtrl = list(maxfun = 100000)) to your model to increase the number of iterations and the warning is gone:

library(lme4)
mm.new = glmer(FirstResponseAccuracy ~ 1 + lag + Aware + SameLocation + lag:Aware +
lag:SameLocation + Aware:SameLocation + lag:Aware:SameLocation +
(1|Subject), family = binomial(link="logit"), data = data_Report,
glmerControl(optimizer="bobyqa", optCtrl = list(maxfun = 100000)))


Check summary(mm.new) for random and fixed effects. To get the ANOVA-type table you wanted, use the mixed() function (afex package) or the Anova() function (car package). For lsmeans, SEs and all pairwise comparisons, use the lsmeans() function (lsmeans package) as suggested above.

• The mixed function indeed sounds like what I need, but when I use "PB" for p values, it gets stuck on calculating the p values (more than 12 hours and still calculating..) According to the documentation of this function, the only other method relevant is "KR", but this one gives an error msg saying: no applicable method for 'KRmodcomp' applied to an object of class "c('glmerMod', 'merMod')" do u have an idea why? I find it hard to believe that there is no way to get p values in glmer for multilevel variables.. Commented Feb 14, 2017 at 10:32
• @EyalAlefOphir How many levels of random effects (Subjects) do you have? In any case you could try method=LRT to see whether that works. You could also try car::Anova(mflicker). Does that work? Commented Feb 14, 2017 at 17:10
• I have 16 subjects. lsmeans does not work, it gives me the following msg: "Error in lsmeansLT(mflicker, c("Aware", "lag", "SameLocation")) : The model is not linear mixed effects model " - the model was created with glmer, and I indeed used the car : : Anova functionm and it worked well. Only thing missing now is the pairwise comparisons Commented Mar 3, 2017 at 11:41
• @EyalAlefOphir It's hard to troubleshoot without a reproducible example... I just tried it again with some of my own data and everything works fine. Commented Mar 4, 2017 at 3:32
• Thanks for the tip! I edited the question and provided a reproducible example Commented Mar 6, 2017 at 11:40

The problem is that glmer is performing a regression analysis (as it is meant to do) when you want an ANOVA framework. You would have the same problem if you were running lm on cross-sectional data. You could figure out the contrast coding, or...

With your glmer model fit, I think a good approach would be to use Anova` in the car package to run the ANOVA. You can the follow-up with post-hoc analyses using the multcomp package; or you can use phia to focus on interactions.

This post might help as well.