Hi!
In a current project, I am fitting loess models to subsets of data in
order to use the loess predicitons for normalization (similar to what
is done in many microarray analyses). While working on this I ran into
a problem when I tried to predict from the loess models and the data
contained NAs or NaNs. I tracked down the problem to the fact that
predict.loess will not return a value at all when fed with such
values. A toy example:
x <- rnorm(15)
y <- x + rnorm(15)
model.lm <- lm(y~x)
model.loess <- loess(y~x)
predict(model.lm, data.frame(x=c(0.5, Inf, -Inf, NA, NaN)))
predict(model.loess, data.frame(x=c(0.5, Inf, -Inf, NA, NaN)))
The behaviour of predict.lm meets my expectation: I get a vector of
length 5 where the unpredictable ones are NA or NaN. predict.loess on the
other hand returns only 3 values quietly skipping the last two.
I was unable to find anything in the manual page that explains this
behaviour or says how to change it. So I'm asking the community: Is
there a way to fix this or do I have to code around it?
This is in R 2.11.1 (Linux), by the way.
Thanks in advance
Philipp
--
Dr. Philipp Pagel
Lehrstuhl für Genomorientierte Bioinformatik
Technische Universität München
Wissenschaftszentrum Weihenstephan
Maximus-von-Imhof-Forum 3
85354 Freising, Germany
http://webclu.bio.wzw.tum.de/~pagel/
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