But I have centred all the dummy variables for the covariates...
2009/6/26 David Winsemius <dwinsem...@comcast.net>:
> Still the same reasons. It is possible to have collinearity without having
> any one column be a multiple of another.
>
>> xyz <- data.frame(x=sample(1:1000, 5), y=sample(1:1000, 5) ,
>> xx=sample(1:1000, 5) ,yy=sample(1:1000, 5) )
>> xyz$z <- xyz$x + xyz$y + xyz$xx
>> solve(xyz)
> Error in solve.default(xyz) :
> system is computationally singular: reciprocal condition number =
> 6.39164e-20
>
> On Jun 26, 2009, at 6:22 AM, Laura Bonnett wrote:
>
>> 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/Varadhan.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,pgu
>>>> 1a,pgu2a,log(agea),firstinta/1000,totsezbasea)
>>>> fita <- crr(snearma$with.Withtime,csaea,covaea,failcode=2,cencode=0)
>>>
>>> and in case b:
>>>>
>>>> covaeb <-
>>>> cbind(sexb,fshb,fdrb,nsignb,eegb,th1b,th2b,stype1b,stype2b,stype3b,sty
>>>> pe4b,stype5b,pgu1b,pgu2b,(ageb/10)^(-1),firstintb,log(totsezbaseb))
>>>> fitb <- crr(snearmb$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.
>
> David Winsemius, MD
> Heritage Laboratories
> West Hartford, CT
>
>
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