Otto,
The current version of ols() is fairly fussy about the
way the predictors are used. I'm not fond of the I()
construction anyway and so I would either use poly()
or define a new predictor as you suggest in your
original post.
See also this thread:
https://stat.ethz.ch/pipermail/r-help/2010-June/241587.html
-Peter Ehlers
On 2010-07-02 4:51, Otto Kässi wrote:
Hi, Lexi!
I am aware that lm() is the standard way to do ols regression in R.
The reason why I opted for rms::ols() is that later on in my work I
need some rms functions which are not available for lm().
In retrospect, I should have mentioned this already in my original
post. Nonetheless, thanks for a good suggestion :-)
Br,
OK
On Fri, Jul 2, 2010 at 1:35 PM, Setlhare Lekgatlhamang<setlha...@bob.bw> wrote:
Try this
Lm(y~X + I(X^2)), data=dd) # this runs OLS regression and it worked for me
Hope it helps
Lexi
-----Original Message-----
From: r-help-boun...@r-project.org [mailto:r-help-boun...@r-project.org] On
Behalf Of Otto Kässi
Sent: Thursday, July 01, 2010 3:28 PM
To: r-help@r-project.org
Subject: [R] rms::ols& I(.) in formulas
Dear R-helpers,
To start I would like to thank Prof. Harrell for package rms. It is
one of the most useful packages for R that I have encountered.
Turning to my problem, I encountered a surprising problem when working
with rms::ols. It seems that insulating terms in a formula by using
I() to insulate terms in a formula seems to occasionally create a bug.
See the example below:
library(rms)
x<- rnorm(100)
y<- rnorm(100)
dd<- data.frame(cbind(x,y))
ols(y ~ x + I(x^2), data=dd)
ols() function gives the error:
Error in if (!length(fname) || !any(fname == zname)) { :
missing value where TRUE/FALSE needed
Has anyone else encountered something similar? Is this a bug or does
this behavior have a reason?
There are of course trivial workarounds: one can either use poly(x, 2)
or save x^2 as a new column to dd, but trying to debug this was a
pain.
With kind regards,
Otto Kassi
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