Utkarsh Singhal wrote: > Hi R, > > > > I am getting this error while trying to use 'lrm' function with nine > independent variables: > > > >> res = > lrm(y1994~WC08301+WC08376+WC08316+WC08311+WC01001+WC08221+WC08106+WC0810 > 1+WC08231,data=y) > > > > singular information matrix in lrm.fit (rank= 8 ). Offending > variable(s): > > WC08101 WC08221 > > Error in j:(j + params[i] - 1) : NA/NaN argument > > > > Now, if I take choose only four independent variables then there is no > error. > > > >> res = lrm(y1994~ WC08221+WC08106+WC08101+WC08231,data=y) > > > > > > But strangely, if I use 'glm', with the family as binomial(logit) and > with the same dataset, it is working perfectly fine. > > > >> res = > glm(y1994~WC08301+WC08376+WC08316+WC08311+WC01001+WC08221+WC08106+WC0810 > 1+WC08231,data=y,family=binomial(logit)) > > > > Any ideas..? > > > > Regards > > Utkarsh
Design's fitting functions are not kind about ignoring parameters associated with singular covariance matrices. In glm you should see a zero for such coefficients. In design you have to delete the singular variables manually. Occasionally you have to tweak the tol argument to lrm. A new function in Hmisc called redun will run a redundancy analysis to help understand the predictor collinearities. Frank > > > > This e-mail may contain confidential and/or privileged i...{{dropped:13}} > > ______________________________________________ > 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 Chair 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.