R help - Jan 2007 - 'Fitting' a model at predefined points

Hi,
 I have a linear model ("mod1 <- lm(V3~V1+V2) and I would like to get
the
model's prediction at values of V1 and V2 not included in the original
sample.

samp <- read.table("data.dat",nrows=100)
attach(samp)
out.poly <- lm(V3 ~ V1 + V2)

If I try to use out.poly to predict values for the function I run into
problems. It seems that it isn't possible to use a new data frame for the
predict() or fitted() functions.

predict(out.poly, data.frame(x=V1, y=V2)) uses the original data
predict(out.poly, data.frame(x=V1, y=V2), newdata=gene.dat)
- uses the original data also, complaining of the decrease in rows.

If any one could point me in the correct direction, it would be appreciated.

Thanks

	[[alternative HTML version deleted]]

You called your variables V1 and V2.  You need to supply new values of V1 
and V2, not x and y.

On Thu, 25 Jan 2007, akintayo holder wrote:
> Hi,
> I have a linear model ("mod1 <- lm(V3~V1+V2) and I would like to
get the
> model's prediction at values of V1 and V2 not included in the original
> sample.
>
> samp <- read.table("data.dat",nrows=100)
> attach(samp)
> out.poly <- lm(V3 ~ V1 + V2)
>
> If I try to use out.poly to predict values for the function I run into
> problems. It seems that it isn't possible to use a new data frame for
the
> predict() or fitted() functions.
>
> predict(out.poly, data.frame(x=V1, y=V2)) uses the original data
> predict(out.poly, data.frame(x=V1, y=V2), newdata=gene.dat)
> - uses the original data also, complaining of the decrease in rows.
>
> If any one could point me in the correct direction, it would be
appreciated.
>
> Thanks
>
> 	[[alternative HTML version deleted]]
>
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>
-- 
Brian D. Ripley,                  ripley at stats.ox.ac.uk
Professor of Applied Statistics,  http://www.stats.ox.ac.uk/~ripley/
University of Oxford,             Tel:  +44 1865 272861 (self)
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