[R] Deviance Residuals

[Iasonas Lamprianou]

Thanks, I'll try to put my hands on the reference
By the way, would it be easier if I just checked out the code by which the glm function computes the residuals? Or maybe this is not a very good idea. And if it is, how can I check out the source, I never really found out!

jason

Dr. Iasonas Lamprianou


Assistant Professor (Educational Research and Evaluation)
Department of Education Sciences
European University-Cyprus
P.O. Box 22006
1516 Nicosia
Cyprus 
Tel.: +357-22-713178
Fax: +357-22-590539


Honorary Research Fellow
Department of Education
The University of Manchester
Oxford Road, Manchester M13 9PL, UK
Tel. 0044  161 275 3485
iasonas.lamprianou at manchester.ac.uk


--- On Fri, 20/8/10, David Winsemius <dwinsemius at comcast.net> wrote:

> From: David Winsemius <dwinsemius at comcast.net>
> Subject: Re: [R] Deviance Residuals
> To: "Iasonas Lamprianou" <lamprianou at yahoo.com>
> Cc: r-help at r-project.org
> Date: Friday, 20 August, 2010, 13:20
> 
> On Aug 20, 2010, at 5:54 AM, Iasonas Lamprianou wrote:
> 
> > Dear all,
> > 
> > I am running a logistic regression and this is the
> output:
> > 
> > glm(formula = educationUniv ~ brncntr, family =
> binomial)
> > 
> > Deviance Residuals:
> >    Min   
>    1Q   Median   
>    3Q      Max  #
> αυτά είναι τα υπόλοιπα
> > -0.8825  -0.7684 
> -0.7684   1.5044   1.6516
> > 
> > Coefficients:
> >            Estimate Std.
> Error z value Pr(>|z|)
> > (Intercept) -1.06869    0.01155
> -92.487   <2e-16 ***
> > brncntrNo    0.32654   
> 0.03742   8.726   <2e-16
> ***
> > ---
> > Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05
> '.' 0.1 ' ' 1
> > 
> > (Dispersion parameter for binomial family taken to be
> 1)
> > 
> > Null deviance: 49363  on 42969  degrees of
> freedom
> > Residual deviance: 49289  on 42968  degrees
> of freedom
> > AIC: 49293
> > 
> > 
> > I thought that the residuals should all be restricted
> in the range 0 to 1 (since I am predicting a binary
> outcome).
> 
> The internal regression calculations are done on the
> log-odds scale so the working residuals are on that scale.
> Those are stored in the glm.obj as the "residuals" item. I
> believe that if you tried mean(glm.obj$residuals) you should
> get 0.  Presumably the deviance residuals are offered
> in preference to the working residuals because the deviance
> residual's use as an influence measure is made readily
> interpretable by reference to chi-square statistics. Page
> 205 of the Hastie and Pregibon citation has all the
> definitions.
> 
> --David.
> 
> 
> 
> > I read many posts on this list and I realized that
> there are four(!?) different types of residuals. I need a
> simple account of these four types of residuals, if anyone
> can help it will be great.
> > 
> > residuals(glm1, "response")
> > residuals(glm1, "pearson")
> > residuals(glm1, "deviance")
> > residuals(glm1, "working") - especially this one
> confuses me a lot!
> > 
> > What is the "working" option and how is this
> different?
> > 
> > Thank you
> > Jason
> > 
> > Dr. Iasonas Lamprianou
> > 
> --
> David Winsemius, MD
> West Hartford, CT
> 
>