Hi folks, I have circular data that I'd like to model as a mixture of a uniform and a Von Mises centered on zero. The data are from 2 groups of human participants, where each participant is measured repeatedly within each of several conditions. So, the data frame would look something like:
######## design = expand.grid( person = 1:20 , variable1 = 1:2 , variable2 = 1:3 , repetition = 1:100 ) design$group = design$person %% 2 ######## where each row would have a data point. Now, I know how to fit the mixture of a uniform and Von Mises to each individual cell of the data independently using the EM algorithm, yielding estimates of the mixture proportion and Von Mises concentration per cell. However, I of course want to assess the degree to which the group and other variables in the design affect these model parameters, and at least in the case of the proportion estimate, I'm uncomfortable submitting the raw proportion to a test that is going to assume Gaussian error (eg. ANOVA, or lmer(..., family=gaussian)). I'm aware that lmer lets one specify non-gaussian links, but as I understand it, if I wanted to, say, specify the binomial link (which seems appropriate for a proportion), lmer wants the data to be the raw 1's and 0's, not the proportion estimate obtained from EM. I've heard that there are hierarchical mixture modelling methods out there (possibly Bayesian hierarchical mixture modelling) that might let me achieve model fitting and inference in one step (eg. model the mixture and influence on each parameter from the between and within-person variables, and treating people as random effects), but I'm having trouble tacking down instructions on how to do this. Any pointers would be greatly appreciated! Cheers, Mike -- Mike Lawrence Graduate Student Department of Psychology Dalhousie University Looking to arrange a meeting? Check my public calendar: http://tr.im/mikes_public_calendar ~ Certainty is folly... I think. ~ ______________________________________________ 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.