R help - Mar 2008 - pseudo R square and/or C statistic in R logistic regression

Dear all,

I am now doing the logistic regression using R. (glm, family=binomial).  Besides
the standardize summary statistics generated from R, I am also interested in
some more informations concerning the model fitting / prediction etc;
Particularly I am interested in "pseudo R squar" and "C
statistic". I searched the R- help and could only get very limited
information. (Post by  Frank Harrell  mentioning about "the somers2 or
rcorr.cens functions in the Hmisc library." ) However, I tried but still
could not get through it. Also I found someone mentioned about do it manually.

Could someone give me a more detailed information on how I could get these two
summary statistics after performed the the logistic regression using glm?


Thank you very much.

***********************************************
Xiaojing Wang    PhD
Research Assistant Professor, Dept. of Oral Biology
School of Dental Medicine, University of Pittsburgh

Suite 500, Bridgeside Point
100 Technology Drive
Pittsburgh, PA 15219
Tel:412-648-9206  Fax:412-648-8779
Email:xiw23@pitt.edu
***********************************************




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Dear all,

 

I am now doing the logistic regression using R. (glm, family=binomial).
Besides the standardize summary statistics generated from R, I am also
interested in some more informations concerning the model fitting /
prediction etc; Particularly I am interested in "pseudo R squar" and
"C
statistic". I searched the R- help and could only get very limited
information. (Post by  Frank Harrell  mentioning about "the somers2 or
rcorr.cens functions in the Hmisc library." ) However, I tried but still
could not get through it. Also I found someone mentioned about do it
manually.

 

Could someone give me a more detailed information on how I could get these
two summary statistics after performed the the logistic regression using
glm?  

 

Thank you very much.

 

***********************************************

Xiaojing Wang    PhD

Research Assistant Professor, Dept. of Oral Biology

School of Dental Medicine, University of Pittsburgh

 

Suite 500, Bridgeside Point

100 Technology Drive

Pittsburgh, PA 15219

Tel:412-648-9206  Fax:412-648-8779

Email:xiw23@pitt.edu

***********************************************

 

 

 


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>From: "Wang, Xiaojing" <xiw23 at pitt.edu>
>Date: 2008/03/26 Wed AM 10:08:01 CDT
>To: "'r-help at r-project.org'" <r-help at
r-project.org>
>Subject: [R] pseudo R square and/or C statistic in R logistic regression
The analogue to the regression R rsquared in a GLM 
is the deviance. I don't know what C is but hopefully someone else does and
will reply.

Also, John Fox's text, "Applied Regression Analysis, Linear Models and
Related Methods" and Modern
Applied Statistics with S, by Bill Venables and Brian Ripley, both do a very
nice job of covering GLM's so I would look at one of those tests for the
technical details. Agresti is the supposed bible but I think those are quite
sufficient.




>Dear all,
>
>I am now doing the logistic regression using R. (glm, family=binomial). 
Besides the standardize summary statistics generated from R, I am also
interested in some more informations concerning the model fitting / prediction
etc; Particularly I am interested in "pseudo R squar" and "C
statistic". I searched the R- help and could only get very limited
information. (Post by  Frank Harrell  mentioning about "the somers2 or
rcorr.cens functions in the Hmisc library." ) However, I tried but still
could not get through it. Also I found someone mentioned about do it manually.
>
>Could someone give me a more detailed information on how I could get these
two summary statistics after performed the the logistic regression using glm?
>
>
>Thank you very much.
>
>***********************************************
>Xiaojing Wang    PhD
>Research Assistant Professor, Dept. of Oral Biology
>School of Dental Medicine, University of Pittsburgh
>
>Suite 500, Bridgeside Point
>100 Technology Drive
>Pittsburgh, PA 15219
>Tel:412-648-9206  Fax:412-648-8779
>Email:xiw23 at pitt.edu
>***********************************************
>
>
>
>
>	[[alternative HTML version deleted]]
>
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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.