I am analyzing the eye-tracking data from a designed experiment. A simplified version of my data looks like this (You can get the dput() data here),
head(lookDATA)
participant fixationImage fixationCount
1 9 Automobile 81
2 9 Bird 63
3 9 Chair 82
4 9 Dog 64
5 9 Face 90
6 9 Plant 75
where participant is a unique identifier for each subject, fixationImage is what picture category they fixated on, and fixationCount is the number of times they fixated on that picture category.
I fit a poisson model to the data using glmer() from the lme4 package.
model<-glmer(fixationCount ~ fixationImage + (1|participant), family = poisson, data = lookDATA)
I used lsmeans() from the lsmeans package to examine the differences among the factor levels,
cld(lsmeans(model,"fixationImage"))
which provides the following output:
fixationImage lsmean SE df asymp.LCL asymp.UCL .group
Chair 3.786022 0.05764923 NA 3.673018 3.899026 1
Bird 3.866201 0.05750641 NA 3.753476 3.978925 2
Dog 3.868768 0.05751010 NA 3.756037 3.981500 2
Body 3.883644 0.06040952 NA 3.765230 4.002059 23
Plant 3.893327 0.05746744 NA 3.780679 4.005975 23
Automobile 3.901939 0.05745528 NA 3.789315 4.014563 23
Face 3.946848 0.05832549 NA 3.832519 4.061178 3
According to my (perhaps limited) understanding of the using lsmeans vignette the lsmean column should represent the average number of looks to a given category predicted by the model.
However, these values seem uncomfortably far from simple descriptive statistics for these numbers,
summaryBy(fixationCount ~ fixationImage, data = lookDATA)
fixationImage fixationCount.mean
1 Automobile 55.18750
2 Bird 53.25000
3 Body 57.12821
4 Chair 50.39450
5 Dog 53.82883
6 Face 56.76389
7 Plant 54.71429
suggesting perhaps that I do not correctly understand what the lsmeans represent here, or perhaps that I've misspecified the model.
Any assistance would be greatly appreciated.