[R] 'Fitting' a model at predefined points
Prof Brian Ripley
ripley at stats.ox.ac.uk
Fri Jan 26 07:47:04 CET 2007
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:
> 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
> samp <- read.table("data.dat",nrows=100)
> 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.
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> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
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)
1 South Parks Road, +44 1865 272866 (PA)
Oxford OX1 3TG, UK Fax: +44 1865 272595
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