I would like to estimate a 95% highest density area for a multivariate parameter space (In the context of anova). Unfortunately I have only experience with univariate kernel density estimation, which is remarkebly easier :)
Using Gibbs, i have sampled from a posterior distirbution of an Anova model with k means (mu) and 1 common residual variance (s2). The means are independent of eachother, but conditional on the residual variance. So now I have a data frame of say 10.000 iterations, and k+1 parameters. I am especially interested in the posterior distribution of the mu parameters, because I want to test the support for an inequalty constrained model (e.g. mu1 > mu2 > mu3). I wish to derive the multivariate 95% highest density parameter space for the mu parameters. For example, if I had a posterior distirbution with 2 means, this should somehow result in the circle or elipse that contains the 95% highest density area. Is something like this possible in R? All tips are welcome. -- View this message in context: http://www.nabble.com/Multivariate-kernel-density-estimation-tp20894766p20894766.html Sent from the R help mailing list archive at Nabble.com. ______________________________________________ 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.
