# [R] Fitting a polynomial using lrm from the Design library

David Winsemius dwinsemius at comcast.net
Fri Jun 18 23:13:33 CEST 2010

```On Jun 18, 2010, at 12:02 PM, Josh B wrote:

> Hi all,
>
> I am looking to fit a logistic regression using the lrm function
> from the Design library. I am interested in this function because I
> would like to obtain "pseudo-R2" values (see http://tolstoy.newcastle.edu.au/R/help/02b/1011.html)
> .
>
> Can anyone help me with the syntax?
>
> If I fit the model using the stats library, the code looks like this:
> model <- glm(x\$trait ~ x\$PC1 + I((x\$PC1)^2) + I((x\$PC1)^3), family =
> binomial)
>
> What would be the equivalent syntax for the lrm function?

Not sure if the code you gave above produces an orthogonal set, but
perhaps this will be meaningful to some of r-help's readers (but not
necessarily to me):

require(Design)
mod.poly3 <- lrm( trait ~ poly(PC1, 3), data=x)

This does report results, but I'm not sure how you would interpret.
(See below for one attempt)

I think Harrell would probably recommend using restricted cubic
splines, however.

mod.rcs3 <- lrm( trait ~ rcs(PC1, 3), data=x)

For plotting with Design/Hmisc functions, you will get better results
> plot(mod3, PC1=NA)
# Perfectly sensible plot which includes the OR=0 line that would be
the theoretically ideal result.

# Whereas plot.Design does not know how to plot the earlier result

> plot(mod.poly3, PC1=NA)
Error in plot.Design(mod.poly3, PC1 = NA) :
matrix or interaction factor may not be displayed

May still get meaningful results with predict:

plot(seq(-3, 2, by=.1), predict(mod.poly3, data.frame(PC1=seq(-3, 2,
by=.1)) ) )

Bit it appears to be less satisfactory that the rcs fit, since it
blows up at the extremes.

--
David.
>
> Thanks very much in advance,
> -----------------------------------
> Josh Banta, Ph.D
> Center for Genomics and Systems Biology
> New York University
> 100 Washington Square East
> New York, NY 10003
> Tel: (212) 998-8465
> http://plantevolutionaryecology.org
>
>
>
>
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