A few issues - Don't let the overall unimportance of a predictor make you worry about non-ordinality (e.g., when scale of plot.xmean.ordinaly has a low range on the y-axis).
We frequently have to face the issue of using an imperfect model by fitting a few variables that don't exactly meet our assumptions, if the majority of variables do. One reason for this is that competing methods may fare worse. Another option is to fit a more flexible model such as the partial proportional odds model. I haven't implemented this in my packages. Another R package may do the job (but without model validation mechanisms provided by rms). Frank apeer wrote: > > I'm not implying they should be discarded; however, at the same time I'm > not certain I fully understand why we should check the ordinality > assumption if in the end we're going to include predictors with which the > response variable behaves in a non-ordinal fashion. > ----- Frank Harrell Department of Biostatistics, Vanderbilt University -- View this message in context: http://r.789695.n4.nabble.com/interpreting-bootstrap-corrected-slope-rms-package-tp3928314p3932923.html Sent from the R help mailing list archive at Nabble.com. ______________________________________________ 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.
