Hi Daniel,
Thanks for the insight. My apologies for the unclearness of my original
question.
I have calculated the fit and se.fit values, see below
predict(fm,newdata=test, se.fit=TRUE, type=c("response"))
If I wasn't mistaking, this would give me the fit values and the standard
errors.
I just have one more question: based on my data, how might I calculate the
expected value of M of the original data set, so that I could compare it to
the M of all the data?
Daniel Malter wrote:
>
> Hi, first, your initial statement of what you wanted to do was obviously
> ambiguous enough to confuse the responders. Therefore, clarity helps
> greatly
> in getting an accurate response. If I understand correctly, you have run
> ONE
> model on whatever data (also often called testing sample). Now you want to
> assess how well this ONE estimate derived from the testing sample predicts
> data from five (or any other arbitrary number of) holdout samples.
>
> In order to do that, the example I have provided works perfectly fine. The
> only thing you do not do is run multiple regressions in the first place.
> Instead you run only one initial regression on the testing sample and
> predict into the holdout samples. This, however, is only a slight change
> of
> the procedure I have outlined. The fit to the holdout samples is assessed
> with measures that any statistics/econometrics book deals with if it has a
> section on prediction.
>
> Best,
> Daniel
> -------------------------
> cuncta stricte discussurus
> -------------------------
>
> -----Ursprüngliche Nachricht-----
> Von: r-help-boun...@r-project.org [mailto:r-help-boun...@r-project.org] Im
> Auftrag von Rbeginner
> Gesendet: Monday, July 20, 2009 10:50 PM
> An: r-help@r-project.org
> Betreff: Re: [R] Re gression for loop test HELP! URGENT!
>
>
> I think the problem is that I've been getting replies about how to make
> new
> regressions, when in fact, I need to use the one I've produced already to
> fit new data, 5 rows at a time, to see if it is also a good representation
> of further data. From the replies, I'm getting the impression that the
> only
> way I can do that is bye producing more regressions and calculating the
> error, but I'm not sure how I should do that, if I get hundreds of new
> regressions. I'm thinking, in my primitive programming terms, that I
> should
> ask the system to run through the new data 5 rows at a time and produce
> some
> indication of deviation (error) from the original regression, which would
> help me decide whether the original regression is is a good representation
> of the new data. Does this make sense?
>
> fm <- lm(M ~ D + O + S) #this is my original regression, and I
> need to use this to fit the test data.
> test = data.frame(Mtest,Dtest,Otest,Stest) #data frame of the test data
> attach(test)
> for (i in 1:1184){
> fmtest <- lm(Mtest ~ Dtest + Otest + Stest, subset=(1:5), data=test)
> print(summary(fmtest)) }
>
> #this would only produce a long string of summaries.
> My data is in the form of
> M D O S
> 1
> 2
> ...
> 1184
>
> Any suggestions?
>
>
>
> Richard Cotton wrote:
>>
>>> I'm new to R, and I've sent this message as a non-member, but since
>>> it's pretty urgent, I'm sending it again now I'm on the mailing list
>>> (Thanks Daniel for your suggestion nevertheless).
>>>
>>> I have calculated a regression in the form of M ~ D + O + S, and I
>>> would like to take this regression and test it with other samples, 5
>>> sets of
>> M, D,
>>> O, and S at a time(I actually have 2000 sets, so it's probably not
>> efficient
>>> to make each a separate set and then index). Since I'll need to test
>>> the regression for 400 groups, I thought a for loop might be
>>> necessary. I've
>> put
>>> everything into a data frame already. Can anyone tell me how to write
>> the
>>> code? I'm especially not sure about how to do the for loop.
>>> And then how would I calculate the error of how well the test samples
>> fit
>>> the original regression?
>>> This is for my internship, so it's very urgent.
>>
>> Take a deep breath, and think calm thoughts. Take a look at the
>> posting guide (http://www.r-project.org/posting-guide.html) - it has
>> useful ideas on thinking through your problem. If you can provide
>> some code then we can see what you want more clearly.
>>
>> Show us how you've done your regression what form your data is in.
>> Tell us which tests you'd like to do on the samples.
>>
>> If you are stuck with for loops, then take a look at section 9.2.2 in
>> the Intro to R guide that comes with R. (Click Help -> Manuals -> an
>> Introduction to R in RGui.)
>>
>> Regards,
>> Richie.
>>
>> Mathematical Sciences Unit
>> HSL
>>
>>
>> ----------------------------------------------------------------------
>> --
>> ATTENTION:
>>
>> This message contains privileged and confidential
>> inform...{{dropped:22}}
>>
>> ______________________________________________
>> 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.
>>
>>
>> -----
>> Regards,
>> Richie.
>>
>> Mathematical Sciences Unit
>> HSL
>>
>
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> ______________________________________________
> R-help@r-project.org mailing list
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> ______________________________________________
> 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
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>
>
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