Dear Juliet,
Why don't you use *apply() instead a for() loop? Here is a starting point:
> apply(myData[,-1],2,function(x) coefficients(lm(response~x)))
var1 var2 var3
(Intercept) 0.10438369 0.10415221 0.1176728
x -0.03354243 0.02429041 -0.2240759
Note that the second row of the code above is the same you get with you
procedure.
See ?apply for more information.
HTH,
Jorge
On Sat, Jan 24, 2009 at 12:30 AM, Juliet Hannah <juliet.han...@gmail.com>wrote:
> Hi All,
>
> I had posted a question on a similar topic, but I think it was not
> focused. I am posting a modification that I think better accomplishes
> this.
> I hope this is ok, and I apologize if it is not. :)
>
> I am looping through variables and running several regressions. I have
> reason to believe that the data is being duplicated because I have
> been
> monitoring the memory use on unix.
>
> How can I avoid this? Here is an example of how I am going about this.
> For lm, I also tried model=FALSE, but this did not seem to do the job.
> Any ideas?
> Thanks for your time.
>
> Regards,
>
> Juliet
>
> # create data
> set.seed(1)
> response <- rnorm(50)
> var1 <- rnorm(50)
> var2 <- rnorm(50)
> var3 <- rnorm(50)
> myData <- data.frame(response,var1,var2,var3)
> var.names <- names(myData)[2:4]
>
>
> numVars <- length(var.names)
> betas <- rep(-1,numVars)
> names(betas) <- var.names
>
> #run regression on var1 through var3.
>
> for (Var_num in 1:numVars)
> {
> col.name <- var.names[Var_num]
> mylm <- lm(response ~ get(col.name),data=myData,model=FALSE)
> betas[Var_num] <- coef(mylm)[2]
> }
>
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