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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