[R] stats lm() function
bartjoosen at hotmail.com
Fri Mar 13 09:35:28 CET 2009
Altough it depends on what crit you keep your variables, but maybe you should
take a look at ?step.
Paul Hermes wrote:
> 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
> 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 .......
> 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" <dwinsemius at comcast.net>
> To: "Paul Hermes" <paul.hermes at analytic-company.com>
> Cc: <r-help at 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:
>>> 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 at r-project.org mailing list
>>> PLEASE do read the posting guide
>>> and provide commented, minimal, self-contained, reproducible code.
>> David Winsemius, MD
>> Heritage Laboratories
>> West Hartford, CT
> R-help at r-project.org mailing list
> PLEASE do read the posting guide
> and provide commented, minimal, self-contained, reproducible code.
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