On 06/18/2010 04:36 PM, David Winsemius wrote:
On Jun 18, 2010, at 5:16 PM, David Winsemius wrote:
On Jun 18, 2010, at 5:13 PM, David Winsemius wrote:
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
with the datadist facilities.
> ddx <- datadist(x)
> options(datadist="ddx")
> plot(mod3, PC1=NA)
Forgot to fix this:
plot(mod.rcs3, PC1=NA)
# Perfectly sensible plot which includes the OR=0 line that would be
the theoretically ideal result.
That would be log(odds) = 0 or OR=1. I wonder how many other errors I
committed?
Best to convert to the rms package - see
http://biostat.mc.vanderbilt.edu/Rrms for differences with Design.
If using ordinary polynomials, use e.g.
mod.poly3 <- lrm(trait ~ pol(PC1, 3), data=x) # not pol not poly
Frank
--
Frank E Harrell Jr Professor and Chairman School of Medicine
Department of Biostatistics Vanderbilt University
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