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,pgu1a,pgu2a,log(agea),firstinta/1000,totsezbasea)
> fita <- crr(snearma$with.Withtime,csaea,covaea,failcode=2,cencode=0)
and in case b:> covaeb <-
cbind(sexb,fshb,fdrb,nsignb,eegb,th1b,th2b,stype1b,stype2b,stype3b,stype4b,stype5b,pgu1b,pgu2b,(ageb/10)^(-1),firstintb,log(totsezbaseb))
> fitb <- crr(snearmb$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
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/Varadhan.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,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
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and provide commented, minimal, self-contained, reproducible code.
Hi!
I guess that you have solved this error by now but I figured I'd post the
result of my 12 hour debugging session in case anyone else has the same
issue. Lets start with a more intuitive example that the one crr offers:
# CODE START #
# Define a set size
my_set_size <- 1000
# Create the covariables
cov <- cbind(rbinom(my_set_size, 1, .5),
rbinom(my_set_size, 1, .05),
rbinom(my_set_size, 1, .1))
dimnames(cov)[[2]] <- c('gender','risk factor 1','risk
factor 2')
# Create random time to failure/cens periods
ftime <- rexp(my_set_size)
# Create events
my_event1 <- rbinom(my_set_size, 1, .04)
my_event2 <- rbinom(my_set_size, 1, .20)
# The competing event can't happen if 1 has already occurred
my_event2[my_event1 > 0] <- 0
fstatus <- my_event1 + my_event2*2
# Factor the censor variable
fstatus <- factor(fstatus, levels=c(0,1,2), labels=c("censored",
"re-operation", "death"))
# Check that it seems Ok
table(fstatus)
# Do the test
test_results <- crr(ftime, fstatus, cov, failcode="re-operation",
cencode="censored")
# Output the results
summary(test_results)
# CODE END #
Ok, so the error occurs in the .Fortran call to "crrval" (I think it
was
called) that returns an empty variable if you forget to specify the factor
failcode, in other words exchange above crr to:
test_results <- crr(ftime, fstatus, cov, failcode=1,
cencode="censored")
And you get the:
Error in solve.default(v[[1]]) :
Lapack routine dgesv: system is exactly singular
Another way to get a singular error is to have a covariate that is 0. Try to
exchange to this code for the covariates:
cov <- cbind(rbinom(my_set_size, 1, .5),
rbinom(my_set_size, 1, .05),
rbinom(my_set_size, 1, .1)*0)
And you get:
Error in drop(.Call("La_dgesv", a, as.matrix(b), tol, PACKAGE =
"base")) :
Lapack routine dgesv: system is exactly singular
This code has been checked with 2.13.1 and cmprsk ver. 2.2.2
I'm not so familiar with R but I believe that this is actually a bug in the
cmprsk package which should check for the variables being factors and then
handle them as expected. I've noticed similar issues with cuminc function
that doesn't behave as expected when providing factored censoring variables.
I haven't seen any issues with factoring the covariates although I've
used
http://www.stat.unipg.it/~luca/R/crr-addson.R Scruccas factor2ind function
when I've had non-binomial factors.
I hope someone out there will be able to avoid my 12 hours of debugging with
this post.
Max Gordon
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