similar to: error in La.svd Lapack routine 'dgesdd'

Dear R users, I have an unbalanced panel with an average of I=100 individuals and a total of T=1370 time intervals, i.e. T>>I. So far, I have been using the plm package. I wish to estimate a FE model like: res<-plm(x~c+v, data=pdata_frame, effect="twoways", model="within", na.action=na.omit) ?where c varies over i and t, and v represents an exogenous impact on x

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))

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]]

Dear R users, I wish to manually demean a panel over time and entities. I tried to code the Wansbeek and Kapteyn (1989) transformation (from Baltagi's book Ch. 9). As a benchmark I use both the pmodel.response() and model.matrix() functions in package plm and the results from using dummy variables. As far as I understood the transformation (Ch.3), Q%*%y (with y being the dependent variable)

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,

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

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

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

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

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

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

Hi everyone, I am doing panel data analysis using the 'plm' package. However, I have noticed that the plm() function reports a different value of R-squared from the R-squared of the lm() function with time-demeaned data. To be clear, I have tried to compute the within model both manually (run an OLS regression with time-demeaned data using lm()) and by using plm(). The two methods give me

Hello again Max. A correction to my response from yesterday. Things were better than they seemed. I thought it over, checked Arellano's panel book and Driscoll and Kraay (Rev. Econ. Stud. 1998) and finally realized that vcovSCC does what you want: in fact, despite being born primarily for dealing with cross-sectional correlation, 'SCC' standard errors are robust to "both

So, I tried the following configure: ./configure --with-lapack --with-blas --with-tcltk No use of ACML at all, but both lapack and blas. Configure proceeds without any errors. Make, same thing. Make check - same problem with stats. But, this time a .fail file got created in tests/Example. Bottom of the file has something which might resonate with someone out there: Error in La.svd(x, nu,

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

I am attempting to perform a pca on a data frame of dimension 5000x19, but when I execute pcapres<-prcomp(pres,center=TRUE) the following error message is returned: Error in La.svd(x, nu, nv, method) : error code 18 from Lapack routine dgesdd Where am I going wrong? I am running R-1.8.0 on Debian. Regards, Laura

Dear R users, Recent changes to the MuMIn package now means that the model averaging command (model.avg) no longer returns confidence intervals, but instead returns zvalues and corresponding pvalues for fixed effects included in models. Previously I have used this package for model selection/averaging following Greuber et al (2011) where it suggests that one should use confidence intervals from

Hi, I'm new to R and stats, and I'm trying to speed up the following sum, for (i in 1:n){ C = C + (X[i,] %o% X[i,]) # the sum of outer products - this is very slow according to Rprof() } where X is a data matrix (nrows=1000 X ncols=50), and n=1000. The sum has to be calculated over 10,000 times for different X. I think it is similar to estimating a co-variance matrix for demeaned

Hello R users I'm using MuMIn but for some reason I'm not getting the adjusted confidence interval and uncoditional SE whe I use model.avg(). I took into consideration the steps provided by Grueber et al (2011) Multimodel inference in ecology and evolution: challenges and solutions in JEB. I created a global model to see if malaria prevalence (binomial distribution) is related to any