> From: Marc Schwartz [mailto:[EMAIL PROTECTED] > Sent: Friday, July 11, 2008 4:54 PM > > on 07/11/2008 02:02 PM Woolner, Keith wrote: > >> From: Marc Schwartz [mailto:[EMAIL PROTECTED] > >> Sent: Friday, July 11, 2008 12:14 PM > >> > >> on 07/11/2008 10:50 AM Woolner, Keith wrote: > >>> Hi everyone, > >>> > >>> Is there a way to take an lm() model and strip it to a minimal form (or > >>> convert it to another type of object) that can still used to predict the > >>> dependent variable? > >> <snip> [...] > If the only thing that you need to do is to use the final models to run > predictions on new data, all you really need is the correct encoding, > contrasts and any transforms of the IV's and the resultant coefficients > from the source models and code your program around those parameters. > > If the models are not going to change 'too frequently' (a relative term > to be sure), I would not worry about spending a lot of time automating > the processes. You can easily hard code the mechanics as they do change > once the basic framework is in place. > > A possibility would be to create a design matrix from the new incoming > data and then use matrix multiplication against the coefficients to > generate the predictions. > > For example, using the very simplistic model from ?predict.lm, we get: > > x <- rnorm(15) > y <- x + rnorm(15) > > my.lm <- lm(y ~ x) > > my.coef <- coef(my.lm) > > > my.coef > (Intercept) x > -0.232839 1.455494 > > # Create some new 'x' data for prediction > new <- data.frame(x = seq(-3, 3, 0.5)) > > # Create a design matrix from the new data > my.mm <- model.matrix(~ x, new) > > # Now create the predicted 'y' values using the new 'x' data > > my.mm %*% my.coef
Thank you, once again, Marc! This turns out to be exactly what I needed. I figured something like this should be possible, but I was fumbling around for the right way to formulate it. Your example was perfect, and much appreciated. Keith ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
