[R] working with ordinal predictor variables?

[Boris Steipe]

This article may be helpful, at least to get you started:

https://www.r-bloggers.com/ordinal-data/

Cheers,
Boris




> On Oct 5, 2017, at 3:35 PM, Bert Gunter <bgunter.4567 at gmail.com> wrote:
> 
> 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.thorn at 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
>> 
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> 
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> 
> ______________________________________________
> R-help at r-project.org mailing list -- To UNSUBSCRIBE and more, see
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> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.