[R] Pseudo R^2 for logit - really naive question

[Frank E Harrell Jr]

Design and Hmisc libraries are not yet on CRAN.  See http://hesweb1.med.virginia.edu/biostat/s for links that will allow you to download them.  -Frank Harrell

On Wed, 7 Aug 2002 08:03:56 -0400
"Paul M. Jacobson" <pmj at jciconsult.com> wrote:

> I could find the function in the function search option on CRAN.  However,
> how to find the actual library remains a mystery to me.  I could not see it
> under the list of available bundles and packages on the CRAN site
> http://lib.stat.cmu.edu/R/CRAN/sources.html
> I am very much a newbie to the R world and need some more direct help.
> 
> -----Original Message-----
> From: owner-r-help at stat.math.ethz.ch
> [mailto:owner-r-help at stat.math.ethz.ch]On Behalf Of Frank E Harrell Jr
> Sent: August 4, 2002 11:36 AM
> To: pmj at jciconsult.com
> Cc: r-help at stat.math.ethz.ch
> Subject: Re: [R] Pseudo R^2 for logit - really naive question
> 
> 
> The Nagelkerke R^2 is commonly used.   The lrm function in the Design
> library computes this for logistic regression.  The numerator is 1 -
> exp(-LR/n) where LR is the likelihood ratio chi-square stat and n is the
> total sample size.  Divide it by the maximum attainable value of this if the
> model is perfect (which is a simple function of the -2 log likelihood with
> an intercept-only model) to get Nagelkerke's R^2.  The numerator is exactly
> the ordinary R^2 in OLS, as LR = -n log(1-R^2) there.  For a more
> interpretable index and one that measures purely discrimination ability, the
> ROC area or "C index" which is essentially a Mann-Whitney statistic based on
> concordance probability is recommended.  The lrm function also outputs this
> or you can get it from the somers2 or rcorr.cens functions in the Hmisc
> library.
> 
> Frank Harrell
> 
> On Sun, 4 Aug 2002 09:08:46 -0400
> "Paul M. Jacobson" <pmj at jciconsult.com> wrote:
> 
> > I am using GLM to calculate logit models based on cross-sectional data.  I
> > am now down to the hard work of making the results intelligible to very
> > average readers.  Is there any way to calculate a psuedo analoque to the
> R^2
> > in standard linear regression for use as a purely descriptive statistic of
> > goodness of fit? Most of the readers of my report will be vaguely familiar
> > and more comfortable with R^2 than with any other regression diagnostics.
> >
> > Paul M. Jacobson
> > Jacobson Consulting Inc.
> > 80 Front Street East, Suite 720
> > Toronto, ON, M5E 1T4
> > Voice:  +1(416)868-1141
> > Farm: +1(519)463-6061/6224
> > Fax: +1(416)868-1131
> > E-mail: pmj at jciconsult.com
> > Web:  http://www.jciconsult.com/
> >
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> 
> --
> Frank E Harrell Jr              Prof. of Biostatistics & Statistics
> Div. of Biostatistics & Epidem. Dept. of Health Evaluation Sciences
> U. Virginia School of Medicine  http://hesweb1.med.virginia.edu/biostat
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> 


-- 
Frank E Harrell Jr              Prof. of Biostatistics & Statistics
Div. of Biostatistics & Epidem. Dept. of Health Evaluation Sciences
U. Virginia School of Medicine  http://hesweb1.med.virginia.edu/biostat
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