[R] linear functional relationships with heteroscedastic & non-Gaussian errors - any packages around?
Jarle Brinchmann
jbrinchmann at gmail.com
Tue Dec 2 18:02:27 CET 2008
[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.
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