On Mon, Aug 30, 2010 at 01:50:03PM +0100, Prof Brian Ripley wrote:
> The underlying problem is your expectations.
>
> R (unlike S) was set up many years ago to use na.omit as the
> default, and when fitting both lm() and loess() silently omit cases
> with missing values. So why should prediction
> What you can do is patch the code to add the NAs back after the
> Prediction step (which many predict() methods do).
Thanks Andy for your hints and especially for digging into the problem
like this! I have, in the meantime, written a simple wrapper around
predict.loess that fills in the NAs, w
The underlying problem is your expectations.
R (unlike S) was set up many years ago to use na.omit as the default,
and when fitting both lm() and loess() silently omit cases with
missing values. So why should prediction from 'newdata' be different
unless documented to be so (which it is nowad
From: Philipp Pagel
>
> 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 a
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
conta
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