covariate data errors
Dear Anthony, The gls (generalized least squares) function in the nlme package should do what you want. (I assume that your analysis leads you to expect an error-covariance matrix of a specific form with some free parameters to estimate.) Generalized least squares estimation is a common topic in regression texts. You'll find a brief appendix on the subject from my R and S-PLUS Companion to Applied Regression, in the context of time-series regression, at <http://www.socsci.mcmaster.ca/jfox/Books/Companion/appendix-timeseries-regression.pdf>. I hope that this helps, John
At 11:40 PM 6/12/2003 -0400, Andy Jacobson wrote:
Greetings,
I would like to fit a multiple linear regression model in
which the residuals are expected to follow a multivariate normal
distribution, using weighted least squares. I know that the data in
question have biases that would result in correlated residuals, and I
have a means for quantifying those biases as a covariance matrix. I
cannot, unfortunately, correct the data for these biases.
It seems that this should be a straightforward task, but so
much of the literature is concerned with the probability model in
which the residuals are uncorrelated that I can't find a good
reference. So in order of importance, please, can someone point me to
a definitive reference for least squares with correlated residuals,
and is there a standard R package to handle this case?
Many thanks in advance,
Anthony
----------------------------------------------------- John Fox Department of Sociology McMaster University Hamilton, Ontario, Canada L8S 4M4 email: jfox at mcmaster.ca phone: 905-525-9140x23604 web: www.socsci.mcmaster.ca/jfox