R 1.6.2 for Windows, Win2k: I have fitted a weighted least squares model using the code
"wls.out <- gls(y ~ x1 + x2 + x3 + x4 + x5 + x6 - 1, data = foo.frame, weights = varConstPower(form = ~ fitted(.), fixed = list(power = 0.5), const = 1))" The data has 62 rows and the response is zero when the covariates are zero. The purpose of the model was to account for the the fact that the variances appear to increase linearly with the fitted values in diagnostic plots. When I use "intervals(wls.out)" R1.6.2 yields the message "Error in intervals.gls(wls7.strat1) : Cannot get confidence intervals on var-cov components: Non-positive definite approximate variance-covariance" Would I be correct in assuming that this means the form of the weighting function is incorrect? How might I examine the estimated variance-covariance matrix? Any suggestions would be greatly appreciated. Respectfully, David Paul ______________________________________________ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help
