I was solving similar problem some time ago. Here is my script. I had a data frame, containing a response and several other variables, which were assumed predictors. I was trying to choose the best linear approximation. This approach now seems to me useless, please, don't blame me for that. However, the script might be useful to you.
<code> library(forward) # dfr is a data.frame, that contains everything. # The response variable is named med5x # The following lines construct linear models for all possibe formulas # of the form # med5x~T+a+height # med5x~a+height+RH # T, a, RH, etc are the names of possible predictors inputs<-names(dfr)[c(10:30,1)] # dfr was a very large data frame, containing lot of variables. # here we have chosen only a subset of them. for(nc in 11:length(inputs)){ # the linear models were assumed to have at least 11 terms # now we are generating character vectors containing formulas. formulas<-paste("med5x",sep="~", fwd.combn(inputs,nc,fun=function(x){paste(x,collapse="+")})) # and then, are trying to fit every for(f in formulas){ lms<-lm(eval(parse(text=f)),data=dfr) cat(file="linear_models.txt",f,sum(residuals(lms)^2),"\n",sep="\t",append=TRUE) } } </code> Hmm, looking back, I see that this is rather inefficient script. For example, the inner cycle can easily be replaced with the apply function. Chris Elsaesser wrote: > > New to R; please excuse me if this is a dumb question. I tried to RTFM; > didn't help. > > I want to do a series of regressions over the columns in a data.frame, > systematically varying the response variable and the the terms; and not > necessarily including all the non-response columns. In my case, the > columns are time series. I don't know if that makes a difference; it > does mean I have to call lag() to offset non-response terms. I can not > assume a specific number of columns in the data.frame; might be 3, might > be 20. > > My central problem is that the formula given to lm() is different each > time. For example, say a data.frame had columns with the following > headings: height, weight, BP (blood pressure), and Cals (calorie intake > per time frame). In that case, I'd need something like the following: > > lm(height ~ weight + BP + Cals) > lm(height ~ weight + BP) > lm(height ~ weight + Cals) > lm(height ~ BP + Cals) > lm(weight ~ height + BP) > lm(weight ~ height + Cals) > etc. > > In general, I'll have to read the header to get the argument labels. > > Do I have to write several functions, each taking a different number of > arguments? I'd like to construct a string or list representing the > varialbes in the formula and apply lm(), so to say [I'm mainly a Lisp > programmer where that part would be very simple. Anyone have a Lisp API > for R? :-}] > > -- View this message in context: http://www.nabble.com/using-lm%28%29-with-variable-formula-tf3772540.html#a10716815 Sent from the R help mailing list archive at Nabble.com. ______________________________________________ R-help@stat.math.ethz.ch 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.