>>> Frank Harrell <f.harr...@vanderbilt.edu> 11/08/2010 17:02:03 >>>
> This problem seems to cry out for one of the many available robust > regression methods in R. Not sure that would be much more appropriate, although it would _appear_ to work. The P&B method is a sort of nonparametric/robust approach to an errors-in-variables problem, intended to provide an indication of consistency of results between two different measurement methods, often with similar error variance. So the aim is to handle the error-in-variable problem at least consistently, to avoid the bias that results from assuming no error in predictors. The M-estimator and related robust regression methods in things like MASS and robustbase don't handle errors in the predictors. Of course, with small errors in predictors that won't matter much; rlm and the like will be pretty much as defensible then as they ever are. But perhaps one could construct a more formal robust equivalent of error-in-variable regression by using a max likelihood functional relationship model with bivariate t (choosing arbitrarily low df) instead of bivariate gaussian errors? Unfortunately I haven't tried that, so no help beyond the thought ... Steve Ellison ******************************************************************* This email and any attachments are confidential. Any use...{{dropped:8}} ______________________________________________ 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.