R help - Dec 2008 - linear functional relationships with heteroscedastic & non-Gaussian errors - any packages around?

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 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.

[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.