I am working (in R 3.2.3 using
lme4 for doing mixed effect modeling) with vowel data from many different subjects. The question I'm interested is whether certain vowels undergo change over time (as in the English word 'I') as a function of the following consonant type, and in particular whether this is the case for all subjects, or only for some. Suppose, for simplicity, that I am only measuring a single vowel in many different words, and that there are no relevant differences between these words. My model would then looks as follows:
measure ~ (1|subject) + timepoint + (timepoint|subject) + following_segment + (following_segment|subject) + timepoint:following_segment + (timepoint:following_segment|subject)
timepoint is a continuous variable referring to the timepoint in milliseconds of each measurement, and
following_segment is a binary variable referring to either a neutral context or a context where I am expecting a change over time.
If I run
ranef on this model and look in the final column, corresponding to the
(timepoint:following_segment|subject) term, I indeed get either very large numbers (e.g. 500 Hz) or very small numbers (e.g. 100 Hz, just to name two random examples; there is a large amount of variability, because these estimates are correlated with the subject's physiological properties such as jaw size).
I now want to know whether these differences in random slope estimates between subjects are significant, for individual subjects (i.e. 'is this subject sensitive to the
following_segment manipulation or not?'). Since I know that
t = B/SE, all I believe I would need is the standard error for the individual participant within this specific term of the model, and then I can use
pnorm to get a p value (please correct me if this is too simple). Regarding obtaining the estimated SE, this question gave me a starting point in suggesting
summary(model)@REmat, which unfortunately returns only
NULL. Another question suggests using
ranef(model,condVar=TRUE), which unfortunately gives an error that
conditional variances not currently available via ranef when there are multiple terms per factor. Is there another way to test whether the individual random slope estimates are significantly different from 0?
Here is a histogram of the random slopes:
The peak around 800 Hz looks interesting; any way I can test for each participant's slope estimates whether they are located around that 800-Hz peak, versus around the big peak near the 0 point?