[snip]
>Discarding actual data points always makes me nervous. Sometimes the points >we want to discard are actually the most interesting. No doubt this is true, and there's a lot of information in those outliers, a lot of structure. For instance, in this case, one part of the outlier population is actually identifiable as a valid part of the primary dataset having the abscissa shifted by a known constant. The mechanism for that is known, so it could be defended that this portion of the outliers could be added back into the main population by removing the shift. Still, not much else is known about its surround, so I/we wonder what else we'd be picking up if that were done. But for the primary application, which is a calibration, I think going after the main population is what's wanted right now. Thanks again. - Jan [snip] ______________________________________________ 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.
