Hello all, I'm currently working on a project where I'm interested in the relationship between two variables that are measured with error, suggesting the need for reduced major axis regression. However, the data structure also necessitates the inclusion of random effects for both variables so I initially thought to use a bivariate-response mixed model. Unfortunately the relevant covariance/correlation isn't quite what I'm interested in for the biological question of interest. The tentative solution I've come up with is to use the variances and covariances to estimate a slope (COVx,y/VARx) and the slope and variable means to calculate the intercept. Since I'm doing this on the posteriors I'm able to get credibility intervals and mode estimates and not have to run a regression on the BLUPs. This gets directly at the question in which I'm interested and does so at the level that is relevant. Does this seem like an appropriate approach? Are there mixed versions of RMA (google didn't reveal anything to me) or other alternatives that seem preferable? Thanks for any feedback and sorry for a bit of rambling and the open ended nature of the query, Ned (slope.me<-posterior.mode(ests.trunc$VCV[,2]/ests.trunc$VCV[,4])) HPDinterval(ests.trunc$VCV[,2]/ests.trunc$VCV[,4]) intercept.me<-ests.trunc$Sol[,1]-slope.me*ests.trunc$Sol[,2] posterior.mode(intercept.me) HPDinterval(intercept.me)
Ned A. Dochtermann Assistant Professor / Department of Biological Sciences NORTH DAKOTA STATE UNIVERSITY p: 701.231.7353 / f: 701.231.7149 / www.ndsu.edu https://sites.google.com/site/neddochtermann/ ned.dochtermann at ndsu.edu --