I would consider this is a question for a statistics forum such as stats.stackexchange.com, not R-help, which is about R programming. They do sometimes intersect, as here, but I think you need to *understand what you're doing* before you write the R code to do it.
Obviously, IMO. Cheers, Bert Bert Gunter "The trouble with having an open mind is that people keep coming along and sticking things into it." -- Opus (aka Berkeley Breathed in his "Bloom County" comic strip ) On Thu, Oct 5, 2017 at 10:54 AM, Alexandra Thorn <alexandra.th...@gmail.com> wrote: > I'm trying to develop a linear model for crop productivity based on > variables published as part of the SSURGO database released by the > USDA. My default is to just run lm() with continuous predictor > variables as numeric, and discrete predictor variables as factors, but > some of the discrete variables are ordinal (e.g. drainage class, which > ranges from excessively drained to excessively poorly drained), but > this doesn't make use of the fact that the predictor variables have a > known order. > > How do I correctly set up a regression model (with lm or similar) to > detect the influence of ordinal variables? > > How will the output differ compared to the dummy variable outputs for > unordered categorical variables. > > Thanks, > Alex > > ______________________________________________ > R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see > 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. > [[alternative HTML version deleted]] ______________________________________________ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see 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.
