Hi Everyone, Thank you for all your comments and suggestions.
I determined that I had a full rank model matrix by using the code: > qr(covaeb)$rank This is 17 which is equal to the number of covariates in the matrix, covaeb. I cannot invert the model matrix using 'solve' as my matrix is not square. In the matrices ending in a, there are 1677 rows and 15 columns/covariates while in the matrices ending in b, there are 701 rows and 17 columns. Thank you, Laura 2009/6/26 Ravi Varadhan <rvarad...@jhmi.edu>: > > How did you determine that you have "full rank" model matrix comprising 17 > predictors? Are you able to invert the model matrix using `solve'? If not, > you still have collinearity problem. > > If you are, then the problem might be in the Newton's method used by `crr' > to solve the partial-likelihood optimization. The hessian matrix of the > parameters might be singular during the iterations. If this is the case, > your best bet would be to just simplify the model, i.e. use fewer > predictors. > > Ravi. > > ---------------------------------------------------------------------------- > ------- > > Ravi Varadhan, Ph.D. > > Assistant Professor, The Center on Aging and Health > > Division of Geriatric Medicine and Gerontology > > Johns Hopkins University > > Ph: (410) 502-2619 > > Fax: (410) 614-9625 > > Email: rvarad...@jhmi.edu > > Webpage: > http://www.jhsph.edu/agingandhealth/People/Faculty_personal_pages/Varadhan.h > tml > > > > ---------------------------------------------------------------------------- > -------- > > > -----Original Message----- > From: Laura Bonnett [mailto:l.j.bonn...@googlemail.com] > Sent: Friday, June 26, 2009 6:22 AM > To: Ravi Varadhan > Cc: r-help@r-project.org > Subject: Re: [R] crr - computationally singular > > Dear Sir, > > Thank you for your response. You were correct, I had 1 linearly dependent > column. I have solved this problem and now the rank of 'covaeb' is 17 > (qr(covaeb)$rank = 17). However, I still get the same error message when I > use covaeb in the 'crr' function. > >> fit=crr(snearmb$with.Withtime,csaeb,covaeb,failcode=2,cencode=0) > 8 cases omitted due to missing values > Error in drop(.Call("La_dgesv", a, as.matrix(b), tol, PACKAGE = "base")) : > system is computationally singular: reciprocal condition number = > 3.45905e-25 > > Are there any other reasons why this may be happening? > > Thank you, > > Laura > > 2009/6/25 Ravi Varadhan <rvarad...@jhmi.edu>: >> This means that your design matrix or model matrix is rank deficient, >> i.e it does not have linearly independent columns. Your predictors are > collinear! >> >> >> Just take your design matrices "covaea" or "covaeb" with 17 predcitors >> and compute their rank or try to invert them. You will see the problem. >> >> Ravi. >> >> ---------------------------------------------------------------------- >> ------ >> ------- >> >> Ravi Varadhan, Ph.D. >> >> Assistant Professor, The Center on Aging and Health >> >> Division of Geriatric Medicine and Gerontology >> >> Johns Hopkins University >> >> Ph: (410) 502-2619 >> >> Fax: (410) 614-9625 >> >> Email: rvarad...@jhmi.edu >> >> Webpage: >> http://www.jhsph.edu/agingandhealth/People/Faculty_personal_pages/Vara >> dhan.h >> tml >> >> >> >> ---------------------------------------------------------------------- >> ------ >> -------- >> >> >> -----Original Message----- >> From: r-help-boun...@r-project.org >> [mailto:r-help-boun...@r-project.org] On Behalf Of Laura Bonnett >> Sent: Thursday, June 25, 2009 11:39 AM >> To: r-help@r-project.org >> Subject: [R] crr - computationally singular >> >> Dear R-help, >> >> I'm very sorry to ask 2 questions in a week. I am using the package 'crr' >> and it does exactly what I need it to when I use the dataset a. >> However, when I use dataset b I get the following error message: >> Error in drop(.Call("La_dgesv", a, as.matrix(b), tol, PACKAGE = "base")) : >> system is computationally singular: reciprocal condition number = >> 1.28654e-24 >> >> This is obviously as a result of a problem with the data but apart >> from dataset a having 1674 rows and dataset b having 701 rows there is >> really no difference between them. >> >> The code I am using is as follows where covaea and covaeb are matrices >> of covarites, all coded as binary variables. >> In case a: >>> covaea <- >>> cbind(sexa,fsha,fdra,nsigna,eega,th1a,th2a,stype1a,stype2a,stype3a,pg >>> u >>> 1a,pgu2a,log(agea),firstinta/1000,totsezbasea) >>> fita <- crr(snearma$with.Withtime,csaea,covaea,failcode=2,cencode=0) >> >> and in case b: >>> covaeb <- >>> cbind(sexb,fshb,fdrb,nsignb,eegb,th1b,th2b,stype1b,stype2b,stype3b,st >>> y >>> pe4b,stype5b,pgu1b,pgu2b,(ageb/10)^(-1),firstintb,log(totsezbaseb)) >>> fitb <- crr(snearmb$with.Withtime,csaeb,covaeb,failcode=2,cencode=0) >> >> csaea and csaeb are the censoring indicators for a and b respectively >> which equal 1 for the event of interest, 2 for the competing risks >> event and 0 otherwise. >> >> Can anyone suggest a reason for the error message? I've tried running >> fitb with variants of covaeb and irrespective of the order of the >> covariates in the matrix, the code runs fine with 16 of the 17 >> covariates included but then produces an error message when the 17th is > added. >> >> Thank you for your help, >> >> Laura >> >> ______________________________________________ >> 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. >> >> > > ______________________________________________ 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.
