Dear all:
By comparing glmresult$y and model.response(model.frame(glmresult)), I
have found out which one is
set to be "TRUE" and which "FALSE".But it seems that to fit a logistic
regression , logit (or logistic) transformation has to be done before
regression.
Does anybody know how to obtain the transformation result ? It is hard
to settle down before knowing the actual process R works . I have read some
books and the "?glm" help file , but what they told me was not sufficient.
Best wishes ,
Bin Yue
Weiwei Shi wrote:
>
> Dear Bin:
> you type
> ?glm
> in R console and you will find the Detail section of help file for glm
>
> i pasted it for you too
>
> Details
>
> A typical predictor has the form response ~ terms where response is the
> (numeric) response vector and terms is a series of terms which specifies a
> linear predictor for response. For binomialand quasibinomial families the
> response can also be specified as a
> factor<file:///Library/Frameworks/R.framework/Versions/2.6/Resources/library/base/html/factor.html>
> (when
> the first level denotes failure and all others success) or as a two-column
> matrix with the columns giving the numbers of successes and failures. A
> terms specification of the form first + second indicates all the terms in
> first together with all the terms in second with duplicates removed. The
> terms in the formula will be re-ordered so that main effects come first,
> followed by the interactions, all second-order, all third-order and so on:
> to avoid this pass a terms object as the formula.
>
> A specification of the form first:second indicates the the set of terms
> obtained by taking the interactions of all terms in first with all terms
> in
> second. The specification first*second indicates the *cross* of first and
> second. This is the same as first + second + first:second.
>
> glm.fit is the workhorse function.
>
> If more than one of etastart, start and mustart is specified, the first in
> the list will be used. It is often advisable to supply starting values for
> a
> quasi<file:///Library/Frameworks/R.framework/Versions/2.6/Resources/library/stats/html/family.html>
> family,
> and also for families with unusual links such as gaussian("log").
>
> All of weights, subset, offset, etastart and mustart are evaluated in the
> same way as variables in formula, that is first in data and then in the
> environment of formula.
>
>
>
> On Dec 5, 2007 10:41 PM, Bin Yue <[EMAIL PROTECTED]> wrote:
>
>>
>> Dear Marc Schwartz:
>> When I ask R2.6.0 for windows, the information it gives does not contain
>> much about family=binomial .
>> You said that there is a detail section of "?glm". I want to read it
>> thoroughly. Could you tell me where and how I can find the detail
>> section
>> of "?glm".
>> Thank you very much .
>> Best regards,
>> Bin Yue
>>
>>
>>
>> Marc Schwartz wrote:
>> >
>> >
>> > On Wed, 2007-12-05 at 18:06 -0800, Bin Yue wrote:
>> >> Dear friends :
>> >> using the "glm" function and setting family=binomial, I got a list
>> of
>> >> coefficients.
>> >> The coefficients reflect the effects of predicted variables on the
>> >> probability of the response to be "1".
>> >> My response variable consists of "A" and "D" . I don't know which
>> level
>> >> of
>> >> the response was set to be 1.
>> >> is the first element of the response set to be 1?
>> >> Thank all in advance.
>> >> Regards,
>> >>
>> >> -----
>> >> Best regards,
>> >> Bin Yue
>> >
>> >
>> > As per the Details section of ?glm:
>> >
>> > For binomial and quasibinomial families the response can also be
>> > specified as a factor (when the first level denotes failure and all
>> > others success) ...
>> >
>> >
>> > So use:
>> >
>> > levels(response.variable)
>> >
>> > and that will give you the factor levels, where the first level is 0
>> and
>> > the second level is 1.
>> >
>> > If you work in a typical English based locale with default alpha based
>> > level ordering, it will likely be A (Alive?) is 0 and D (Dead?) is 1.
>> >
>> > HTH,
>> >
>> > Marc Schwartz
>> >
>> > ______________________________________________
>> > 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.
>> >
>> >
>>
>>
>> -----
>> Best regards,
>> Bin Yue
>>
>> *************
>> student for a Master program in South Botanical Garden , CAS
>>
>> --
>> View this message in context:
>> http://www.nabble.com/logistic-regression-using-%22glm%22%2Cwhich-%22y%22-is-set-to-be-%221%22-tf4953617.html#a14185819
>> 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.
>>
>
>
>
> --
> Weiwei Shi, Ph.D
> Research Scientist
> GeneGO, Inc.
>
> "Did you always know?"
> "No, I did not. But I believed..."
> ---Matrix III
>
> [[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.
>
>
-----
Best regards,
Bin Yue
*************
student for a Master program in South Botanical Garden , CAS
--
View this message in context:
http://www.nabble.com/logistic-regression-using-%22glm%22%2Cwhich-%22y%22-is-set-to-be-%221%22-tf4953617.html#a14187112
Sent from the R help mailing list archive at Nabble.com.
______________________________________________
R-help@r-project.org mailing list
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PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
