On Jan 29, 2011, at 12:12 PM, Russell Pierce wrote:
Just in case someone else stumbles onto this thread and is facing a
similar issue: The quick solution for me turned out to be using Glm
and Predict in the rms package. Thanks go to Joshua and Ista for
helping me out with this issue. Double thanks go to Joshua for
suggesting I take a closer look at the rms package.
library(rms)
dat <- data.frame(xxA = rnorm(20,10), xxB = rnorm(20,20))
dat$out <- with(dat,xxA+xxB+xxA*xxB+rnorm(20,20))
rms.res <- Glm(out ~ scale(xxA)*scale(xxB),data=dat)
newdata <- as.data.frame(Predict(rms.res,xxA=c(-1,0,1),xxB=c(-1,0,1))
[,1:3])
Puzzled that you would see a particular need for the rms functions
(not that I have anything against the rmes package, but it doesn't
seem to be needed here.)
This seems to work without error:
rms.res <- glm(out ~ scale(xxA)*scale(xxB), data=dat)
newdata <- predict(rms.res,
newdata=data.frame(xxA=c(-1,0,1),xxB=c(-1,0,1)))
--
David Winsemius, MD
West Hartford, CT
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