[R] logistic model diagnostics residuals.lrm {design}, residuals()

[Greg Snow]

Why do you need a diagnostic that has properties from the normal?  Logistic regression is based on binary (binomial distribution) data, not continuous data.  Any transform that forced normality (even just under a given null hypothesis) would probably distort any real information that might be gleaned. 

What are you really trying to accomplish?  It is probably easier to address that then to do an artificial 'normalization'.

-- 
Gregory (Greg) L. Snow Ph.D.
Statistical Data Center
Intermountain Healthcare
greg.snow at imail.org
801.408.8111


> -----Original Message-----
> From: r-help-bounces at r-project.org [mailto:r-help-bounces at r-
> project.org] On Behalf Of Chaudhari, Bimal
> Sent: Thursday, March 11, 2010 10:10 AM
> To: r-help at r-project.org
> Subject: [R] logistic model diagnostics residuals.lrm {design},
> residuals()
> 
> I am interested in a model diagnostic for logistic regression which is
> normally distributed (much like the residuals in linear regression with
> are ~ N(0,variance unknown).
> 
> My understanding is that most (all?) of the residuals returned by
> residuals.lrm {design} either don't have a well defined distribution or
> are distributed as Chi-Square.
> 
> Have I overlooked a residual measure or would it be possible to
> transform one of the residual measures into something reasonably
> 'normal' while retaining information from the residual so I could
> compare between models (obviously I could blom transform any of the
> measures, but then I'd always get a standard normal)?
> 
> Cheers,
> bimal
> 
> Bimal P Chaudhari, MPH
> MD Candidate, 2011
> Boston University
> MS Candidate, 2010
> Washington University in St Louis
> 
> 
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> 
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