One relatively easy way would be to construct some dummy data with the
appropriate names and formula, use lm() to construct the model object, and
then change the coefficients in the model object to be what you want (e.g.,
by fit$coefficients[2] <- 0.79). This should then work with
predict(). Don't make the mistake of thinking that output than output from
anything other than predict(obj, newdata=data) will mean anything sensible
-- summary(), predict(, se=T), ...
If your data is all numeric, then the easiest way might be just to use
matrix computations:
pred <- cbind(1, data) %*% coef
where data is n x k matrix, coef is a vector of length k+1, and the
intercept is the first element of coef.
hope this helps,
Tony Plate
At Tuesday 10:04 AM 1/6/2004 -0500, Alexander Wise
wrote:>Hello, this probably seems like an odd question, but...
>
>If I have the formula and the coefficients for a linear model that I would
>like to apply to some data using predict() -- is there a way I construct
>an object of type lm such that predict() will work with it? Or another way
>besides predict()?
>
>Alexander Wise
>Senior Software Engineer
>http://laser.cs.umass.edu
>
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Tony Plate tplate at acm.org