similar to: prcomp - error code 18

Dear R helpers, I am working with R 2.4.1 GUI 1.18 (4038) for MacOSX. I have a matrix of 10 000 genes and try to run the following commands: > model.mix<-makeModel (data=data, formula=~Dye+Array+Sample+Time, random=~Array+Sample) > anova.mix<-fitmaanova (data, model.mix) > test.mix<-matest (data, model=model.mix, term="Time", n.perm=100, test.method=c(1,0,1,1))

Dear all I have a big matrix of standardized values (dimensions 285x5829) and R fails to calculate the principal components using prcomp() with the following error message: pc <- prcomp(my.matrix) Error in La.svd(x, nu, nv, method) : error code 1 from Lapack routine dgesdd Is the matrix too big? I'm using R-1.8.1 under Unix (Solaris8) and Linux(Suse 8.2). I tried to perform a principal

Hello R-users, during one of my analyses that involve a SVD, I get the following error message: Error in La.svd(x, nu, nv, method) : error code 1 from Lapack routine dgesdd With a search on the R web site, I only found references to error codes 17 and 3 for this particular routine. I also found the Lapack web site, but could not find a list of the possible error messages. If somebody knows what

Dear All, Under Debian GNU/Linux La.svd (with method = "dgesdd") sometimes gives the error "Error in La.svd(data, nu = 0, nv = min(nrow, ncol), method = "dgesdd") : LAPACK routine DGESDD gave error code -12" It seems not to depend on the data per se, but on the relationship between numbers of rows and columns. For example, if the number of columns is 100,

Hi, I'm just wondering if it is planned to include the Lapack routine DGESDD (and friends) in R-1.4.0? This is faster (supposedly by a factor of ~6 for large matrices) than DGESVD which is currently (R-1.3.1) called by La.svd. And if it is not in the plans yet, is there a chance it could be? I've added it to my local version of R-1.3.1 and so far see a factor of 4 improvement over

Lately I'm getting this error quite a bit: Error in La.svd(x, nu, nv) : error code 1 from Lapack routine 'dgesdd' I'm running R 2.5.0 on a 64 bit Intel machine running Fedora (8 I think). Maybe the 64 bit platform is more fragile about declaring convergence. I'm seeing way more of these errors than I ever have before. From R-Help I see that this issue comes up from time to

Error in La.svd(x, nu, nv) : error code 1 from Lapack routine ‘dgesdd’ what resources are there to track down errors like this [[alternative HTML version deleted]]

Well, CentOS 5.3, which amounts to the same thing. I recently decided to upgrade my main research machine from Fedora Core 8 -> CentOS 5.3. Basically, I was looking to move to a distro with longer 'term-of-life' than the release schedule for Fedora currently allows. The machine is a multi-Opteron box, so both 32- and 64-bit apps natively supported. Since I do a lot of 'linear

Dear R community, I have a data matrix (531X314), and would like to apply the prcomp. However, I got this error Lapack message. I am using R3.2.2. I googled a bit and found that it might be related to converge issue. ?Just wonder if there is a way to get around it? Thank you very much! Ace On Thursday, December 29, 2016 11:44 AM, Ista Zahn <istazahn at gmail.com> wrote: Use

Hi, I wonder where I can find an example of using a function in LAPACK library in a user's own C code. I wrote a C program which will be compiled and linked to produce a DLL file and then loaded into R. I hope to use a function from LAPACK library, for example, dgesdd, in the program. Following R manual, I call the function by F77_CALL(dgesdd) in the program. The program can be compiled

I agree with Kasper, this is a 'big' issue. Does your method of taking only n PCs reduce the load on memory? The new addition to the summary looks like a good idea, but Proportion of Variance as you describe it may be confusing to new users. Am I correct in saying Proportion of variance describes the amount of variance with respect to the number of components the user chooses to show? So

Dear all, Has anyone seen the same problem? I tried compiling R-1.9.0 alpha (2004-03-16) on our Opteron box running SUSE Linux ES8. I ran: ./configure --enable-R-shlib --with-blas=goto and got: Source directory: . Installation directory: /usr/local C compiler: gcc -m64 -O2 -g -msse2 -march=k8 -Wall --pedantic C++ compiler: g++ -m64 -O2 -g

Dear Philipp, this is just a tentative answer because debugging is really not possible without a reproducible example (or, at a very bare minimum, the output from traceback()). Anyway, thank you for reporting this interesting numerical issue; I'll try to replicate some similar behaviour on a similarly dimensioned artificial dataset when I have some time (which might not be soon). As for now,

Dear all, The 'make check' step fails for the pacakge mva on IBM AIX. The tail of the Rout log file looks like: > for(factors in 2:4) print(update(Harman23.FA, factors = factors)) Call: factanal(factors = factors, covmat = Harman23.cor) Uniquenesses: height arm.span forearm lower.leg weight 0.170 0.107 0.166

Per the discussion about the problems with prcomp() when n << p, which boils down to a problem with svd() when n << p, here is a patch to prcomp() which substitutes La.svd() instead of svd(). -Greg (This is really a feature enhancement, but submitted to R-bugs to make sure it doesn't get lost. ) *** R-1.6.0/src/library/mva/R/prcomp.R Mon Aug 13 17:41:50 2001 ---

Generator Microsoft Word 11 (filtered medium) Hi, I'm the mannova package maintainer. We used La.svd(X, method="dgesvd") in maanova package before. After R-2.3.0, the old La.svd() method was deprecated for option method="dgesvd". I changed maanova code correspondingly, which will call method="dgesdd" instead. But after that, we keep getting below error message

Full_Name: Ryszard Czerminski Version: 1.8.1 OS: GNU/Linux Submission from: (NULL) (205.181.102.120) prcomp(..., scale = TRUE) does not work correctly: $ uname -a Linux 2.4.20-28.9bigmem #1 SMP Thu Dec 18 13:27:33 EST 2003 i686 i686 i386 GNU/Linux $ gcc --version gcc (GCC) 3.2.2 20030222 (Red Hat Linux 3.2.2-5) > a <- matrix(rnorm(6), nrow = 3) > sum((scale(a %*% svd(cov(a))$u, scale

According to R help: princomp() uses eigenvalues of covariance data. prcomp() uses the SVD method. yet when I run the (eg., USArrests) data example and compare with my own "hand-written" versions of PCA I get what looks like the opposite. Example: comparing the variances I see: Using prcomp(USArrests) ------------------------------------- Standard deviations: [1] 83.732400 14.212402

Full_Name: Juha Heljoranta Version: R 2.1.1 (2005-06-20) OS: Gentoo Linux Submission from: (NULL) (88.112.29.250) prcomp has a bug which causes following error Error in svd(x, nu = 0) : infinite or missing values in 'x' on a valid data set (no Infs, no missing values). The error is most likely caused by the zeros in data. My code and temporary workaround: m = matrix(... ...

Dear expeRts, I have run some simulations under R 2.15.1 on a Mac, and I have rerun a sample of them under R 3.0.1 on Windows (and also for comparison under R2.14.1 on Windows). For most cases, I get exactly the same results in all three runs. However, for those cases that depend on principal components computed with prcomp, where the particular choice of the orthogonalization is arbitrary