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
[[alternative HTML version deleted]]
______________________________________________
R-help@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
