[apologies if this appears twice] Hi,
I have a situation where I have a set of pairs of X & Y variables for each of which I have a (fairly) well-defined PDF. The PDF(x_i) 's and PDF(y_i)'s are unfortunately often rather non-Gaussian although most of the time not multi-modal. For these data (estimates of gas content in galaxies), I need to quantify a linear functional relationship and I am trying to do this as carefully as I can. At the moment I am carrying out a Monte Carlo estimation, sampling from each PDF(x_i) and PDF(y_i) and using a orthogonal linear fit for each realisation but that is not very satisfactory as it leads to different linear relationships depending on whether I do the orhtogonal fit on x or y (as the errors on X & Y are quite different & non-Gaussian using the covariance matrix isn't all that useful either) Does anybody know of code in R to do this kind of fitting in a Bayesian framework? My concern isn't so much on getting _the_ best slope estimate but rather to have a good estimate of the uncertainty on the slope. Cheers, Jarle. ______________________________________________ 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.