Dear All, A situation that for sure happens very often: suppose you are in the following situation
set.seed(1235) x1 <- seq(30) x2 <- c(rep(NA, 9), rnorm(19)+9, c(NA, NA)) x3 <- c(rnorm(17)-2, rep(NA, 13)) y <- exp(seq(1,5, length=30)) mm<-lm(y~x1+x2+x3) i.e. you try a simple linear regression with multiple regressors which exhibit some missing values. This is what happens to me while working with some time series which I use as regressors and whose missing values are padded with NAs. lm, as a default, disregard the sets of incomplete observations and therefore drops quite a lot of data. Is there any way to circumvent this? I mean, is there a way to somehow come up with a piecewise linear regression where, whenever possible, all the 3 regressors are used but we switch to 1 or 2 when there are missing data? I say this because it is totally unfeasible to try to figure out the values of the missing data in my regressors, but at the same time I cannot restrict my model to the intersection of the non-NA values in the 3 regressors. If this makes sense, do I have to code it myself or is there any package which already implemented this? Any suggestion is appreciated. Cheers Lorenzo ______________________________________________ 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.
