Dear list members, I want to add a predictor to a linear model without changing the coefficients of the existing model. How is that done with R?
So if I have a response y and predictors x1, x2, x3 I want to make a model lm1 like lm1 = lm(y~x1+x2) After this model is computed I want to add x3 like lm2 = lm(y~x1+x2+x3) However, unlike it is done by the notation above or by update or add1 (as far as I understand) I don't want a new model with all predictors estimated anew but I want a model lm2 where the coefficients for x1 and x2 stay exactly as in lm1 and the coefficent for x3 is estimated additionally. The reasons for this are theoretical. I guess what I want is similar to calculating a new regression on the residuals of lm1. lm2 = lm(residuals(lm1)~x3) however, I would prefer to to that in the common framework of the lm command in order to calculate statistics, perform anova on the models and so on. thanks for your help! Urs ______________________________________________ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see 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.
