[R] Logistic regression and R

[ONKELINX, Thierry]

Dear Marlene,

Have a look at the polr() function in the MASS package.

HTH,

Thierry
 


----------------------------------------------------------------------------
ir. Thierry Onkelinx
Instituut voor natuur- en bosonderzoek / Research Institute for Nature and Forest
Cel biometrie, methodologie en kwaliteitszorg / Section biometrics, methodology and quality assurance
Gaverstraat 4
9500 Geraardsbergen
Belgium
tel. + 32 54/436 185
Thierry.Onkelinx at inbo.be
www.inbo.be

To call in the statistician after the experiment is done may be no more than asking him to perform a post-mortem examination: he may be able to say what the experiment died of.
~ Sir Ronald Aylmer Fisher

The plural of anecdote is not data.
~ Roger Brinner

The combination of some data and an aching desire for an answer does not ensure that a reasonable answer can be extracted from a given body of data.
~ John Tukey

-----Oorspronkelijk bericht-----
Van: r-help-bounces at r-project.org [mailto:r-help-bounces at r-project.org] Namens marlene marchena
Verzonden: vrijdag 31 juli 2009 12:04
Aan: r-help
Onderwerp: Re: [R] Logistic regression and R

Hi there,



I take advantage of this chat to ask other question related to logistic regression. This is my first time as well.

I have data that I want to model but I'm not sure if glm() is the correct function to use. My problem is as follow, I used Oxford Instability Score of the shoulder (OIS, independent variable) as indicative of the outcome ( 12-20 best, to 41-60 worst outcome, 5 possible results). Looking for many independent variables like categorical and numerical I want to see their prognostic impact on the outcome.

There is a function which I can use to model my problem? I heard about multinomial logistic regression but I did not able to find nothing related to it.



Any help would be much appreciated.


Marlene.

2009/7/31 G. Jay Kerns <gkerns at ysu.edu>

> Dear Carlos,
>
> On Thu, Jul 30, 2009 at 6:11 PM, Carlos López<natorro at fisica.unam.mx>
> wrote:
> > Hello everybody :-)
> >
> > I have some data that I want to model with a logistic regression, 
> > most of the independent variables are numeric and the only dependent 
> > is
> categorical,
> > I was thinking that I could apply a logistic regression using glm 
> > but I wanted to deepen my knowledge of this so I tried to do some 
> > reading and found the "iris" dataset, now I would like to ask two 
> > things, first if
> you
> > know of any bibliography to read more about the logistic regression 
> > and R
> so
> > I could understand and interpret better the output,
>
>
> See the following
>
> https://home.comcast.net/~lthompson221/<https://home.comcast.net/%7Elt
> hompson221/>
>
> and the following specific link on that page:
>
> https://home.comcast.net/~lthompson221/Splusdiscrete2.pdf<https://home
> .comcast.net/%7Elthompson221/Splusdiscrete2.pdf>
>
> which is a manual to accompany Agresti's _Categorical Data Analysis_.
> In particular, you may want to check out Chapter 5 (and also some of 
> 4).
>
>
> >and second, what could I
> > do when I have some independent variables that are not only 
> >numerical but  categorical too, i.e. mixed (categorical and 
> >numerical), can I still use
> a
> > logistic regression?
>
> Easy peasy, lemon squeezy.  See page 78.
>
> Hope this helps,
> Jay
>
>
>
>
>
>
>
>
>
>
>
>
> ***************************************************
> G. Jay Kerns, Ph.D.
> Associate Professor
> Department of Mathematics & Statistics Youngstown State University 
> Youngstown, OH 44555-0002 USA
> Office: 1035 Cushwa Hall
> Phone: (330) 941-3310 Office (voice mail)
> -3302 Department
> -3170 FAX
> VoIP: gjkerns at ekiga.net
> E-mail: gkerns at ysu.edu
> http://www.cc.ysu.edu/~gjkerns/ <http://www.cc.ysu.edu/%7Egjkerns/>
>
> ______________________________________________
> R-help at 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.
>

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