similar to: Lapack, determinant, multivariate normal density, solution to linear system, C language

Dear list, I am a little puzzled by computing time in connection with calling C functions. With the function mysolve1 given below I solve Ax=B, where the actual matrix operation takes place in mysolve2. Doing this 5000 times takes 3.51 secs. However, if I move the actual matrix inversion part into mysolve1 (by uncommenting the two commented lines and skip the call to mysolve2) then the

-----Original Message----- From: R-SIG-Debian [mailto:r-sig-debian-bounces at r-project.org] On Behalf Of Rolf Turner Sent: Thursday, 19 January 2017 10:11 AM To: Ian Erickson Cc: r-sig-debian at r-project.org Subject: Re: [R-sig-Debian] [FORGED] Taking determinant of a matrix of NAs results in intermittent memory corruption >On 19/01/17 11:54, Ian Erickson wrote: >> Greetings; I've

Hello, Currently, determinant(A) calculates the determinant of 'A' by factorizing A=LU and computing prod(diag(U)) [or the logarithm of the absolute value]. The factorization is done by LAPACK routine DGETRF, which gives a status code INFO, documented [1] as follows: *> INFO is INTEGER *> = 0: successful exit *> < 0: if INFO = -i, the i-th

Hello, I am trying to write an R package for doing analysis of speaker recognition systems. The big thing in this line of research is a DET-plot (detection Error Trade-off, a ROC plot with qnorm() warped axes). My approach has been to make a class "det" and also a function "det()" that will prepare the data into the right class. Now loading the library doesn't like me

Hello, When I use det() and qr() on complex matrices the result is in some cases indeterministic. The documentation speaks of numeric matrices (and not of complex matrices) but det() uses qr() which should handle complex matrices correctly. I've also tried using only qr() with similar results. det() returns a value that is not the determinant of the complex matrix (in accordance with

Full_Name: Krzysztof Podgorski Version: R version 2.4.1 (2006-12-18) OS: Windows XP Submission from: (NULL) (130.235.3.79) The function ''det'' works improperly for a singular matrix and returns a non-zero value even if ''solve'' reports singularity. The matrix is very simple as shown below. A <- diag(rep(c(64,8), c(8,8))) A[9:16,1] <- 8 A[1,9:16] <- 8

Dear R-devel, I am trying to use Fortran DGESV subroutine into C. Here it is the relevant part of the C file I am currently writing #include<stdio.h> #include<R.h> #include<Rmath.h> #include<math.h> void F77_NAME(DGESV)( int*, int*, double*, int*, int*, double*, int*, int*); void solve( int *p, double *A, double *Ainv) { ... F77_CALL(DGESV)(p, p, Ain, p, ipiv,

Dear R-users, I computed determinant of a square matrix "var.r" using the SVD output: detr _ 1 d _ svd(var.r)$d for (i in 1:length(d)) { detr _ detr*d[i] } print(detr) 30.20886 BUT when I tried : det(var.r) I got : -30.20886 Is this because SVD output will only give absolute of the eigenvalues ?, If this is the case how can I get the original eigenvalues? Thanks, Agus

Hello, I would like to estimate a VAR model with HAC corrected standard errors. I tried to do this by using the sandwich package, for example: > library(vars) > data(Canada) > myvar = VAR(Canada, p = 2, type = "const") > coeftest(myvar, vcov = vcovHAC) Error in umat - res : non-conformable arrays Which suggests that this function is not compatible with the VAR command.

[Hope this is the right list where to send...] An attempt to update package 'mnormt' involves the addition of a small new function called 'pd.solve'. When I come to the package checking stage, an error occurs in parsing pd.solve.Rd. The full transcript of the outcome is copied below (it includes details on my installation) but the critical point is where the \examples{} section

-----Original Message----- From: Dirk Eddelbuettel [mailto:dirk.eddelbuettel at gmail.com] On Behalf Of Dirk Eddelbuettel Sent: Thursday, 19 January 2017 11:21 AM To: Klint Gore Cc: r-sig-debian at r-project.org Subject: Re: [R-sig-Debian] Taking determinant of a matrix of NAs results in intermittent memory corruption >So this converges towards 'old versions bad, new versions fine' ?

Hello there I'm writing some c-code to solve a numerically tough problem for me in R. Looking in Lapack.h, i find the following line F77_NAME(dgesvx)(const int* fact, const char* trans, const int* n, and I believe that "fact" should've been char instead of int, i.e. F77_NAME(dgesvx)(const char* fact, const char* trans, const int* n, My reasoning: In the R-source:

Greetings I'm a total newbie in R and I'm trying to make a comparisson of Excel and R in the fields of: - optimisation modeling (using solver) - decision trees - simulation modeling as described in Winston, Wayne L.: Practical Management Science. for optimisation modeling in Excel I would normaly use solver. In R however I can't seem to be able to find the solution. I've

Hi uRsers, when inverting a 2 by 2 matrix using solve, I encountered a error message: solve.default(sigma, tol = 1e-07) : system is computationally singular: reciprocal condition number = 1.7671e-017 and then I test the determinant of this matrix: 6.341393e-06. In my program, I have a condition block that whether a matrix is invertible like this: if(det(sigma)<1e-7) return NULL;

Dear R-help list, Pls I have this problem. Suppose I have a matrix of size nxn say, generated as follows   z<-matrix(rnorm(n*n,0,1),nrow=n)   I want to write a function such that for i in 1:n, I will remove the rows and columns corresponding to i (so, will be left with n-1*n-1 submatrix in each cases). Now I need the sum of the determinant of each of this submatrices. As an example, if n=3, it

Dear R-users, a question concerning sparse matrices in package "spam" (spam_0.29-2). On one hand I have a spam object (n X n) from which I cannot compute the inverse. On the other hand, if I convert this object in a plain matrix, I can find the inverse without any problem. Specifically I get the following error message: Error in chol.spam(a, ...) : Singularity problem when

Hi there, Could anyone please help me to understand what should be done in order not to get this error message: Error: evaluation nested too deeply: infinite recursion / options(expressions=)? Here is my code: determinant<- function(x){det(matrix(c(1.0,0.2,0.5,0.8,0.2,1.0,0.5,0.6,0.5,0.5,0.5,1.0,x,0.8,0.6,x,1.0),ncol=4,byrow=T))} matrix<-

Dear R users, even if this question is not related to an issue about R, probably some of you will be able to help me. I have a square matrix of dimension k by k with alpha on the diagonal and beta everywhee else. This symmetric matrix is called symmetric compound matrix and has the form a( I + cJ), where I is the k by k identity matrix J is the k by k matrix of all ones a = alpha - beta c =

Dear R, If I have remembered correctly, a square matrix is singular if and only if its determinant is zero. I am a bit confused by the following code error. Can someone give me a hint? > a <- matrix(c(1e20,1e2,1e3,1e3),2) > det(a) [1] 1e+23 > solve(a) Error in solve.default(a) : system is computationally singular: reciprocal condition number = 1e-17 Thanks in advance! Feng --

I just detected, that det() is not working on complex matrices any more, due to the fix to the bug reports noted above. I am not happy with this, as determinants are perfectly usable on complex matrices. AFAIUI the bugs resulted from less than optimal behaviour of qr() in certain cases. IMHO this is due to the unhappy decision to use a default for parameter tol to decide whether the the