similar to: A matrix is full rank is equal to having independent columns?

Dear R-listers, I have a dxr matrix Z, where d > r. And the product Z*Z' is a singular square matrix. The problem is how to get the left inverse U of this singular matrix Z*Z', such that U*(Z*Z') = I? Is there any to figure it out using matrix decomposition method? Thanks a lot for your help. Fred

Hey, R-listers I was recommended to try using smooth.spline function for estimating 2-Dimensinal curve given a data set. So will you please tell me where to get this R function? Or which package provides this function? Thanks for your point. Fred

Hi, R-listers I tried to plot several graphs in a sigle x-y coordinate settings, like the following: |(y) s | ****** s | ***** s | sssssssssssssssssss |_______________________________(x) where "*" and "s" denote two diffrent plots. However, when I used plot(data1); % data1 is the data points of "*"

Hey, I want to draw several plots sequently, but have to make them dispaly in one figure. So how to achieve this? Thanks. Fred

Dear All, I have a function which takes a n x m matrix as an argument and returns an n x n matrix. I want to take bootstrap samples form the input matrix in the way as each row represent a multivariate observation, so each bootstrap sample would be an n x m matrix, and on each sample I want to calculate the n x n matrix. This task can be done with the sample function, but I would like to use

Given an nxm matrix A I want to compute the nxm matrix B whose ij-th element is the sum of the elements of A lying on the diagonal that ends with element ij, i.e., b_ij = a_ij + a_(i-1)(j-1) + a_(i-2)(j-2) + ... In APL (which I no longer use), I would use the 'rotate' operator to derive an array whose columns are diagonals of the given array and then cumulate down columns. Is

I am trying to write an efficient function that will do the following: Given an nxm matrix, 10 rows (observations) by 10 columns (samples) for each row, test of all values in the row are greater than a value k If all values are greater than k, then set all values to NA (or something), Return an nxm matrix with the modified rows. If I do this with a matrix of 20,000 rows, I will be waiting until

Hello, I am trying to find a permutation test that works on a general nxm table. The data set is small enough to have cells with too small counts to make chi2-approximation invalid. If the table was a 2x2 contingency table I would like to use a Fsher exact test (fisher.test) but that wont work in this general table. Does there exist a general function for this test. Best regards, Magnus

I believe eigen(), svd() and qr() can all do it. Andy > From: Jonathan Baron > > On 06/17/04 19:04, Fred wrote: > >Dear R-listers: > > > >I am trying to test an algorithm on a set of linearly > independent vectors > >{x1,x2,...,xn}. > > Well, here's an idea, for 10 vectors of length 10, > as columns of a matrix m1. The 11th seems to be needed.

Hey, R-listers, I have a question about determining the orthogonal basis vectors. In the d-dimensinonal space, if I already know the first r orthogonal basis vectors, should I be able to determine the remaining d-r orthognal basis vectors automatically? Or the answer is not unique? Thanks for your attention. Fred

On 3 May 2018, at 22:09, David Zarzycki via llvm-dev <llvm-dev at lists.llvm.org> wrote: > > Hello, > > In an effort to help LLVM-style projects save memory, I’ve been toying with some macros that provide an alternative to C++ vtables that use LLVM-style RTTI design patterns instead. Is this something that LLVM or sub-projects think is worth pursuing? Or are the macros below

Hello R community, I am using R for creating a model using optimization. I would like to ask if there is R-function/package for solving the problem below: Minimize sum(abs(exp^(Ai1 x1 + Ai2 x2 + ... + Aim xm - bi) - 1)), for each i = 1, ..., n. subject to Ai1 x1 + Ai2 x2 + ... + Ajm xm - bi <= c, where c is a scalar. (x is a vector of variables, A is nxm matrix, b is a vector)

Hey, R-listers I want to get some statistical books on Cumulants for studying. So will you please give me some suggestions on these related books? Thanks for your point. Fred

Dear R-listers: I am trying to test an algorithm on a set of linearly independent vectors {x1,x2,...,xn}. So anyone can give me a hint on generating these vectors in an easy way? Thanks for your points. Fred [[alternative HTML version deleted]]

Hey, All In principal component analysis (PCA), we want to know how many percentage the first principal component explain the total variances among the data. Assume the data matrix X is zero-meaned, and I used the following procedures: C = covriance(X) %% calculate the covariance matrix; [EVector,EValues]=eig(C) %% L = diag(EValues) %%L is a column vector with eigenvalues as the elements percent

R-listers: I may be asking too much from R, but is there a way to use time indexing on a time series object. For instance: > tsobject <- ts(1:12, start =1999, freq = 4) > tsobject Qtr1 Qtr2 Qtr3 Qtr4 1999 1 2 3 4 2000 5 6 7 8 2001 9 10 11 12 > tsobject[1999,Qtr4] Error in NextMethod("[") : Object "Qtr4" not found I would

Dear Fred, If x1 and x2 are *not* normally distributed, you can use independent component analysis (ICA) which is based on the idea that x will be "more normal" than either x1 and x2 following the central limit theorem. See package(fastICA) by JL Marchini, C Heaton, and BD Ripley for details. HTH Thomas > -----Original Message----- > From: Feng Zhang [mailto:f0z6305 at

Le 22/01/2018 ? 17:40, Keith O'Hara a ?crit?: > This behavior is noted in the qr documentation, no? > > rank - the rank of x as computed by the decomposition(*): always full rank in the LAPACK case. For a me a "full rank matrix" is a matrix the rank of which is indeed min(nrow(A), ncol(A)) but here the meaning of "always is full rank" is somewhat confusing. Does it

Le 23/01/2018 ? 08:47, Martin Maechler a ?crit?: >>>>>> Serguei Sokol <sokol at insa-toulouse.fr> >>>>>> on Mon, 22 Jan 2018 17:57:47 +0100 writes: > > Le 22/01/2018 ? 17:40, Keith O'Hara a ?crit?: > >> This behavior is noted in the qr documentation, no? > >> > >> rank - the rank of x as

Hi. I am aware of three different R packages for linear programming: glpk, linprog, lpSolve. From what I can tell, if there are N variables and M constraints, all these solvers require the full NxM constraint matrix. Some linear solvers I know of (not in R) have a sparse matrix input format. Are there any linear solvers in R that have a sparse matrix input format? (including the