search for: solving

I haven't found a function that directly calculates the matrix inverse, does it exist? Otherwise what would be the fastest way to do it? Patrik Waldmann -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info", "help", or "[un]subscribe" (in the

...; > found that qr.solve(A, b) "is == but much better than solve(A) %*% b". > > > (I guess that 'better than' refers to numerical stability?) > > > The "better than" refers to the fact that creating the inverse is > > more-or-less equivalent to solving n systems of linear equations, > > where n is the number of columns in A. If n is large it does not make > > sense to compute the solutions to n systems of equations in order to > > evaluate the solution to one system of equations. > > I see! But then I think the help page...

Hi Guys, I would like to share my experience when installing the Spatial views packages for R. I could not install 32 packages which are parts of the Spatial views, and I use google-search and search to solve ALL those problems for about 2 days. I hope maybe somebody would benefit from my experience. I admitted that I do not have excellent programming skills at all. So, perhaps some of

Hello, I would like to use mapply to avoid using a loop but for some reason, I can't seem to get it to work. I've included copies of my code below. The first set of code uses a loop (and it works fine), and the second set of code attempts to use mapply but I get a "subscript out of bounds" error. Any guidance would be greatly appreciated. Xj, Yj, and Wj are also lists, and s2,

The solve function in r-patched has been changed so that it applies a tolerance when using Lapack routines to calculate the inverse of a matrix or to solve a system of linear equations. A tolerance has always been used with the Linpack routines but not with the Lapack routines in versions 1.7.x and 1.8.0. (You can use the optional argument tol = 0 to override this check for computational

Hello! Can anyone explain me what solve() function does: Gaussian elimination or iterative, numeric solve? In addition, I would need both the Gaussian elimination and iterative solution for the course. Are the two built in R? Thanks! PM

I have a situation in R where I would like to find any x (if one exists) that solves the linear system of equations Ax = b, where A is square, sparse, and singular, and b is a vector. Here is some code that mimics my issue with a relatively simple A and b, along with three other methods of solving this system that I found online, two of which give me an error and one of which succeeds on the simplified problem, but fails on my data set(attached). Is there a solver in R that I can use in order to get x without any errors given the structure of A? Thanks for your time. #CODE STARTS HEREA = as(...

Dear all, The background is that I'm trying to fix this bug in the geometry package: https://r-forge.r-project.org/tracker/index.php?func=detail&aid=1993&group_id=1149&atid=4552 Boiled down, the problem is that there exists at least one matrix X for which det(X) != 0 and for which solve(X) fails giving the error "system is computationally singular: reciprocal condition

I was playing around with a simple example using solve.qp ( function is in the quadprog package ) and the code is below. ( I'm not even sure there if there is a reasonable solution because I made the problem up ). But, when I try to use solve.QP to solve it, I get the error that D in the quadratic function is not positive definite. This is because Dmat is zero because I don't have a

Full_Name: Ravi Varadhan Version: 2.8.1 OS: Windows Submission from: (NULL) (162.129.251.19) Inverting a matrix with solve(), but using LAPACK=TRUE, gives erroneous results: Here is an example: hilbert <- function(n) { i <- 1:n; 1 / outer(i - 1, i, "+") } h5 <- hilbert(5) hinv1 <- solve(qr(h5)) hinv2 <- solve(qr(h5, LAPACK=TRUE)) all.equal(hinv1, hinv2) #

> > Hello All, > Please help me with this problem.I have been having this problem for over a month now and I could not find any information.I later realised that error is with MATRIX package. I am working on graph object using IGRAPH version 0.5.2-2 package & wanted to do Bonacich Power. What I have is undirected GRAPH. This is my graph object. The file 'Bonacich

To R-helpers, R offers the polyroot function for solving mentioned equations iteratively. However, Dr Math and Mathworld (and other places) show in detail how to solve mentioned equations non-iteratively. Do implementations for R that are non-iterative and that solve mentioned equations exists? Regards, Mads Jeppe

...a situation in R where I would like to find any x (if one >exists) that solves the linear system of equations Ax = b, where A is >square, sparse, and singular, and b is a vector. Here is some code that >mimics my issue with a relatively simple A and b, along with three >other methods of solving this system that I found online, two of which >give me an error and one of which succeeds on the simplified problem, >but fails on my data set(attached). Is there a solver in R that I can >use in order to get x without any errors given the structure of A? >Thanks for your time. >#COD...

...t; > I have a situation in R where I would like to find any x (if one exists) that solves the linear system of equations Ax = b, where A is square, sparse, and singular, and b is a vector. Here is some code that mimics my issue with a relatively simple A and b, along with three other methods of solving this system that I found online, two of which give me an error and one of which succeeds on the simplified problem, but fails on my data set(attached). Is there a solver in R that I can use in order to get x without any errors given the structure of A? Thanks for your time. > #CODE STARTS HEREA...

...tuation in R where I would like to find any x (if one exists) > that solves the linear system of equations Ax = b, where A is square, > sparse, and singular, and b is a vector. Here is some code that mimics my > issue with a relatively simple A and b, along with three other methods of > solving this system that I found online, two of which give me an error and > one of which succeeds on the simplified problem, but fails on my data > set(attached). Is there a solver in R that I can use in order to get x > without any errors given the structure of A? Thanks for your time. > #COD...

ABCD are four matrix. A * Inverse((Transpose(A)*Tranpose(B)*B*A+C)) * Transpose(A) * Transpose(B) * D how to write in R in an efficient way? --------------------------------- [[alternative HTML version deleted]]

Why do x<-b%*%ginv(A) and x<-solve(A,b) give different results?. It seems that I am missing some basic feature of matrix indexing. e.g.: A<-matrix(c(0,-4,4,0),nrow=2,ncol=2) b<-c(-16,0) x<-b%*%ginv(A);x x<-solve(A,b);x Thanks in advance, Angel

Full_Name: John Peters Version: 1.5.1 OS: Windows 2000 Submission from: (NULL) (130.155.2.3) solve(a,b) with two arguments gives an error if a is complex and b is a vector: > a_matrix(c(1,2+3i,3,2),ncol=2) > a [,1] [,2] [1,] 1+0i 3+0i [2,] 2+3i 2+0i > b_c(2,2+1i) > solve(a,b) Error in solve.default(a, b) : A must be a complex matrix > is.complex(a) [1] TRUE >

Dear List: I am having some trouble reproducing some GLS estimates using matrix operations that I am not having with other R procedures. Here are some sample data to see what I am doing along with all code: mu<-c(100,150,200,250) Sigma<-matrix(c(400,80,16,3.2,80,400,80,16,16,80,400,80,3.2,16,80,400),n c=4) sample.size<-100 temp <-

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