Altough it depends on what crit you keep your variables, but maybe you should take a look at ?step.
Bart Paul Hermes wrote: > > ok, > i think i have to be more precise of what we are doing. > first thing: this code is not from me, and Im new to R (and never touched > anything like this) > Im just the lucky guy who has to maintain this crap :) > this call to the lm function is part of a code wich is used to predict the > marketvalues from a bunch of our products. > as 'target' function it gets the past marketvalues we have in our > database.(this is what goes into the 'data' parameter into the lm > function) > > then we have allot other prices and enviromental data (like similar > products, stock sizes, seasonal informations, .... ) > with this, the big formula is created (y ~ x1 + x2 + x3 + x4 + x5 ....... > + > x300) > > > all this goes into the lm call. then the result is somehow anaylsed to > figure out wich input data-set had the least influence (or similaryti ) to > the past marketvalues. this one gets eleminated and lm is called again > wihout this data-set. > this is done until we just have a small number of datasets left. > > could be that everything im writing here is totaly bullshit (cause im not > shure if i got every thing right) > but this thing is working an creates very nice predictions ;) > > i just fugured that the lm call's in this loop tooks the most time and i > want to reduce this. > any ideas? > > ----- Original Message ----- > From: "David Winsemius" <dwinsem...@comcast.net> > To: "Paul Hermes" <paul.her...@analytic-company.com> > Cc: <r-help@r-project.org> > Sent: Thursday, March 12, 2009 3:42 PM > Subject: Re: [R] stats lm() function > > >>I think you will find that many readers of this list would rather try to >>dissuade you from this misguided strategy. You are unlikely to get to a >>sensible solution in using step-down procedures with this sort of >>situation (large number of predictors with modest size of data). >> >> -- >> David Winsemius >> >> On Mar 12, 2009, at 1:59 PM, Paul Hermes wrote: >> >>> Hi, >>> >>> Im using the lm() function where the formula is quite big (300 >>> arguments) and the data is a frame of 3000 values. >>> >>> This is running in a loop where in each step the formula is reduced by >>> one argument, and the lm command is called again (to check which >>> arguments are useful) . >>> >>> This takes 1-2 minutes. >>> Is there a way to speed this up? >>> i checked the code of the lm function and its seems that its preparing >>> the data and then calls lm.Fit(). i thought about just doing this >>> praparing stuff first and only call lm.fit() 300 times. >>> [[alternative HTML version deleted]] >>> >>> ______________________________________________ >>> R-help@r-project.org 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. >> >> David Winsemius, MD >> Heritage Laboratories >> West Hartford, CT > > ______________________________________________ > R-help@r-project.org 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. > > -- View this message in context: http://www.nabble.com/stats-lm%28%29-function-tp22483608p22492199.html Sent from the R help mailing list archive at Nabble.com. ______________________________________________ R-help@r-project.org 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.