I am working on applying Sequential Multiple Decision Procedure to genetic association studies of complex disease. Province in 2000 (http://www.ncbi.nlm.nih.gov/pubmed/11108641) developed the method for QTL linkage and Province and Zhang extended it to association with quantitative traits in 2005. My interest is in binary outcomes.
The original formulation of the SMDP (Bechhofer, Kiefer and Sobel, 1968) was predicated on comparing parameters from Koopman-Darmois distributions. In that text, they present solutions for normal, exponential, Bernoulli, poisson and negative binomial distributions. Province and Zhang exploited the fact that residuals from linear regression are normally distributed to apply SMDP to the analysis of these residuals (specifically, they use the case of comparing the variances of several random variables drawn from normal distributions with the same known mean, 0, and unknown, unequal variances to identify the set of random variables/residuals with the smallest variance). My hope initially had been that I might be able to essentially replicate this approach with an analogous measure/residual, but this has proven to be more difficult than anticipated. -bimal > -----Original Message----- > From: Frank E Harrell Jr [mailto:f.harr...@vanderbilt.edu] > Sent: Thursday, March 11, 2010 12:25 PM > To: Chaudhari, Bimal > Cc: r-help@r-project.org > Subject: Re: [R] logistic model diagnostics residuals.lrm {design}, > residuals() > > Chaudhari, Bimal wrote: > > 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 > > Hi Bimal, > > What would make it necessary for the residuals to have a certain > distribution? Why would you expect a categorical Y variable to give > risk to residuals with a nice distributions? > > You can do residual diagnostics without worrying about the > distribution. > > Frank > > > > > 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. > > > > > -- > Frank E Harrell Jr Professor and Chairman School of Medicine > Department of Biostatistics Vanderbilt > University ______________________________________________ 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.
