On Fri, Apr 01, 2005 at 09:56:01AM -0500, Gabor Grothendieck wrote: > Try this: > > my.df <- data.frame(a=1:10, b=11:20, c=21:30, d=31:40) > > model.response(model.frame(cbind(a,b) ~ c+d, my.df)) > a b > 1 1 11 > 2 2 12 > 3 3 13 > 4 4 14 > 5 5 15 > 6 6 16 > 7 7 17 > 8 8 18 > 9 9 19 > 10 10 20
Well I learned something. I didn't know that you could have a multivariate response, but that doesn't actually address my problem. I also have some other variables, which I call "predecessor" variables, that also need to go in the data frame. The problem is basically that the R formula language is just too limiting (unless you are of the "all statistics is regression" school, which I am not). In this application, I am just over the border. I have "response" variables, "predecessor" variables, and "predictor" variables (all need to be vectors of the same length or matrices with the appropriate row dimension, just the usual requirement for data frames). I want the user to be able to use the formula language to connect the "predictor" variables to the linear predictor parameter in the usual way. But in order to get any calculations done, I need to get these other variables -- including the "predecessor" variables, which have no place in the R formula language (!!) -- into a data frame (if I am going to use the reshape function on the data). Moreover, it is not the "S way" to force the user to construct this data frame herself. The variables in the formula (and out of the formula) can just be anywhere, and R is supposed to "do the right thing". So I'm still looking for > > ... a function that > > just stuffs all that stuff into a data frame (model.frame would do it > > if I didn't have this extra stuff). -- Charles Geyer Professor, School of Statistics University of Minnesota [EMAIL PROTECTED] ______________________________________________ R-devel@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-devel
