David Kikuchi <dwkikuchi <at> gmail.com> writes: > > Hi all, > > I'm modeling the probability that a subject attacks or rejects a prey > item based on its proportion of yellow coloration and size. There are > two populations of prey, one defended and the other undefended, so > subjects should reject one type and accept others. Each subject has a > unique rejection threshold that is a line on a contour plot with > coloration and size on the x and y axes. I want to estimate the error > around that line's slope, and believe that I need to estimate two random > slopes per subject to do so, one in the color dimension and the other in > the size dimension. The code that I think I should use to do this is: > glmer(attack ~ prop.color + size + (prop.color + size|subject, family = > binomial), but I cannot find a reference or example for fitting random > slopes in different continuous dimensions. I would appreciate any > pointers in the right direction. > > Thanks, > David
This seems perfectly reasonable. It might be more on-topic on r-sig-mixed-mod...@r-project.org (send follow-ups there please). My only concern with this model is the size of your data set -- you probably need a reasonably large number of trials per subject (20-30, or more?), and a reasonably large number of subjects (at least 10, preferably >20?) in order to estimate the among-subject variation in the response reasonably well. You should be able to use lme4's simulate method to simulate data and try a power analysis -- the hardest part is figuring out what the 'theta' (random-effects) parameters mean (the among-subject variance-covariance matrix is a 3*3 matrix (intercept, color, size), the parameters fill in a lower-triangular matrix t1 0 0 t2 t4 0 t3 t5 t6 which is multiplied by its transpose t1 t2 t3 0 t4 t5 0 0 t6 to get the variance-covariance matrix. ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.