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,
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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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108L, 108L, 108L, 108L, 108L, 108L, 108L, 108L, 108L, 108L,
108L, 108L, 108L, 108L, 108L, 115L, 115L, 115L, 115L, 115L,
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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,
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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, 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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1975L, 1976L, 1979L, 1985L, 1991L, 1994L, 1995L, 1996L, 1998L,
1999L, 2002L, 2010L, 2016L, 2018L, 2020L, 2028L, 2033L, 2043L,
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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,
7813L, 7816L, 7819L, 7821L, 7827L, 7830L, 7831L, 7834L, 7839L,
7841L, 7843L, 7851L, 7853L, 7857L, 7864L, 7869L, 7879L, 7884L,
7897L, 7902L, 7904L, 7905L, 7911L, 7912L, 7913L, 7915L, 7919L,
7922L, 7926L, 7932L, 7933L, 7937L, 7939L, 7947L, 7954L, 7957L,
7959L, 7962L, 7968L, 7969L, 7974L, 7980L, 7984L, 7988L, 7992L,
7995L, 7996L, 8031L, 8033L, 8036L, 8038L, 8044L, 8049L, 8058L,
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??? `
anova(mflicker)
? $\endgroup$ – Roland Feb 13 '17 at 7:34