[R] crr - computationally singular

[Ravi Varadhan]

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.

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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: rvaradhan at jhmi.edu

Webpage:
http://www.jhsph.edu/agingandhealth/People/Faculty_personal_pages/Varadhan.h
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-----Original Message-----
From: Laura Bonnett [mailto:l.j.bonnett at googlemail.com] 
Sent: Friday, June 26, 2009 6:22 AM
To: Ravi Varadhan
Cc: r-help at 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 <RVaradhan at 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: rvaradhan at jhmi.edu
>
> Webpage:
> http://www.jhsph.edu/agingandhealth/People/Faculty_personal_pages/Vara
> dhan.h
> tml
>
>
>
> ----------------------------------------------------------------------
> ------
> --------
>
>
> -----Original Message-----
> From: r-help-bounces at r-project.org 
> [mailto:r-help-bounces at r-project.org] On Behalf Of Laura Bonnett
> Sent: Thursday, June 25, 2009 11:39 AM
> To: r-help at 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
>
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