Test whether random slopes are significantly different from 0 for individual subjects 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)

where 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?
 A: The ranef(model,condVar=TRUE) method is not working because you have the random effects written in separate parenthesis blocks. In this specification, no covariances are estimated among the random slopes (because covariances are only included for terms that share a parenthesis block), but covariances are estimated between the the random intercept and each random slope (because each parenthesis block implicitly also includes the intercept). That's fine, but another totally reasonable specification (although with more parameters) is to allow all the random effects to have nonzero covariance. In other words, you can rewrite the model syntax as
measure ~ timepoint*following_segment + (timepoint*following_segment|subject)

For this rewritten model, the ranef(model,condVar=TRUE) method should work.
If you don't want to rewrite the model in this way and include the extra covariance parameters, then another way to implement the tests you want is via bootstrapping. In each iteration, sample with replacement from each subject's data (do not mix observations between subjects), fit your mixed model, and save the array of random effects. After some large number of iterations (1000? 10000?), you will have distributions for each subject's random effects, which you can compare to 0. Note that this could take a while for your complex model!
