similar to: calculation of U and V matrix of SVD decomposition (according to LINPACK, X = UDV')

Hello All, Although I have downloaded some libraries such as multivariate data analysis library (multiv) and ade4, their functions such as pca or reconst are not recognised. Should I install any thing else or use any instruction so that R could find the location of libraries? Thanks for your quick response, Haleh Yasrebi

Hello, Why do I get doubt (NA) in the factor of test classification even if I fix l (minimum vote)? By setting l, no doubt should be occurred. Look forward to your reply Haleh

Ordinary functions should not alter their inputs but in R-2.15.2 svd(LINPACK=TRUE,X) does. (It worked in 2.15.0 but not in 2.15.1 or 2.15.2 and became deprecated in 2.15.2.) > X <- matrix(c(1,2,3, 5,7,11, 13,17,19), 3, 3) > X [,1] [,2] [,3] [1,] 1 5 13 [2,] 2 7 17 [3,] 3 11 19 > svd(X, LINPACK=TRUE)$d [1] 31.9718214 2.3882717 0.3143114 Warning message:

After fixing princomp(), recently, {tiny negative eigen-values are possible for non-negative definite matrices} Fritz Leisch drew my attention to the fact the not only eigen() can be funny, but also svd(). Adrian Trappleti found that the singular values returned can be "-0" instead of "0". This will be a problem in something like sd <- svd(Mat) $ d

Dear Dr ; I am a PhD student at Epidemiology department of National University of Singapore. I used R command (rcspline.plot) for plotting restricted cubic spline ??? the model is based on Cox. I managed to get a plot without adjustment for other covariates, but I have a problem regarding to adjusting the confounders. I applied below command to generate the matrix for

Dear R list users, the singluar value decomposition of a symmetric matrix M is UDV^(T), where U = V. La.svd(M) gives as output three elements: the diagonal of D and the two orthogonal matrices u and vt (which is already the transpose of v). I noticed that the transpose of vt is not exactly u. Why is that? thank you for your attention and your help Stefano AVVISO IMPORTANTE: Questo messaggio di

Hello, I wanted to install MergeMaid package in v 2.2.1. I could install it but couldn't use without its dependant, Biobase. at biobase installation, I got the following error message In method for function "split": expanding the signature to include omitted arguments in definition: drop = "missing" Error in .MakeSignature(new("signature"), def, signature) :

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

Hello, I try to test joining new RODC (samba-tool domain join unn.global RODC -U Administrator -d5) and it's fail with message: Could not find machine account in secrets database: Failed to fetch machine account password for UNN from both secrets.ldb (Could not find entry to match filter: '(&(flatname=UNN)(objectclass=primaryDomain))' base: 'cn=Primary Domains': No

Hallo, we have 5 ADDCs. All of them did run with sernet-samba 4.6.7. I updated 4 of them to sernet-samba 4.7.0, one after the other, checked replication, everything seemed to be ok. One day later a colleague wanted to delete a lot of users with a powershell-script and since then the replication doesnt work anymore. (Im sure the script is not the problem, but it seemes like it triggered something)

I have a Master's student working on a project which involves estimating parameters of a certain model via maximum likelihood, with the maximization being done via optim(). A phenomenon has occurred which I am at a loss to explain. If we use certain pairs of starting values for optim(), it simply returns those values as the ``optimal'' values, although they are definitely not

Dear R Developers, I should have had the Design library running in R by now but have kept putting off changing some calls to LINPACK routines to use those builtin to R. Specifically I call dqrsl1 and dqr. Would it be an easy task to put those in the next release of R? If not I'll finally bite the bullet and get back into reading LINPACK documentation (which I have but haven't examined

Hi, I understand that the glm.fit calls LINPACK fortran routines instead of LAPACK because it can handle the 'rank deficiency problem'. If my data matrix is not rank deficient, would a glm.fit function which runs on LAPACK be faster? Would this be worthwhile to convert glm.fit to use LAPACK? Has anyone done this already?? What is the best way to do this? I'm looking at very

> On 9 Feb 2017, at 16:00, G?ran Brostr?m <goran.brostrom at umu.se> wrote: > > In my package 'glmmML' I'm using old C code and linpack in the optimizing procedure. Specifically, one part of the code looks like this: > > F77_CALL(dpoco)(*hessian, &bdim, &bdim, &rcond, work, info); > if (*info == 0){ > F77_CALL(dpodi)(*hessian,

Thanks to all who answered my third question. I learned something, but: On 2017-02-09 17:44, Martin Maechler wrote: > >>> On 9 Feb 2017, at 16:00, G?ran Brostr?m <goran.brostrom at umu.se> wrote: >>> >>> In my package 'glmmML' I'm using old C code and linpack in the optimizing procedure. Specifically, one part of the code looks like this:

> On 10 Feb 2017, at 14:53, G?ran Brostr?m <goran.brostrom at umu.se> wrote: > > Thanks to all who answered my third question. I learned something, but: > > On 2017-02-09 17:44, Martin Maechler wrote: >> >>>> On 9 Feb 2017, at 16:00, G?ran Brostr?m <goran.brostrom at umu.se> wrote: >>>> >>>> In my package 'glmmML'

Hello, I try to migrate to dovecot-lda from procmail, but some script can't run under lda cmusieve plugin. dovecot version: dovecot-1.0(debian) revelant config dovecot.conf: protocol lda { postmaster_address = postmaster at wizard.dunanet.hu hostname = wizard.dunanet.hu mail_plugin_dir = /usr/lib/dovecot/modules/lda #sendmail_path = /usr/lib/sendmail auth_socket_path =

I am reading the Mining of Massive Datasets Book by Rajaraman and Ullman. It has a good explanation of Recommendation System at Chapter 9. But what are the relationship between 1) SVD (Singular Decomposition) 2) UV-Decomposition 3) NMF (Non-negative Matrix Factorization) In particular, it seems 2) and 3) can be very similar. Is it right? Thanks. -- View this message in context:

Hi list, Does anyone know if there is a library in R that does MTM-SVD method for spectral analysis? Thanks ----- Yasir H. Kaheil Columbia University -- View this message in context: http://www.nabble.com/Spectral-analysis-with-mtm-svd-Multi-Taper-Method-Combined-with-Singular-Value-Decomposition-tp21671934p21671934.html Sent from the R help mailing list archive at Nabble.com.

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