similar to: A question on orthogonal basis vectors

Hi, Why the qr() produces a negative Q compared with Gram-Schmidt? (note example below, except Q[2,3]) Here is an example, I calculate the Q by Gram-Schmidt process and compare the output with qr.Q() a <- c(1,0,1) b <- c(1,0,0) c <- c(2,1,0) x <- matrix(c(a,b,c),3,3) ########################## # Gram-Schmidt ########################## A <- matrix(a,3,1) q1 <-

Hi: Does R have a function as gsorth is SAS, that perform a the Gram-Schmidt orthonormal factorization of the m ?n matrix A, where m is greater than or equal to n? That is, the GSORTH subroutine in SAS computes the column-orthonormal m ?n matrix P and the upper triangular n ?n matrix T such that A = P*T. or any other version of Gram-Schmidt orthonormal factorization? I search the help, but I

Hello List, I would like to orthonormalize vectors contained in a matrix X taking into account row weights (matrix diagonal D). ie, I want to obtain Z=XA with t(Z)%*%D%*%Z=diag(1) I can do the Gram-Schmidt orthogonalization with subsequent weighted regressions. I know that in the case of uniform weights, qr can do the trick. I wonder if there is a way to do it in the case of non uniform

Hi everyone. Based on a dependent variable (y), I'm trying to generate some independent variables with a specified correlation. For this there's no problems. However, I would like that have all my "regressors" to be orthogonal (i.e. no correlation among them. For example, y = x1 + x2 + x3 where the correlation between y x1 = 0.7, x2 = 0.4 and x3 = 0.8. However, x1, x2 and x3

Hi, I have eight vectors that I would like to orthogonalize preferably using R. The vectors are of considerable length, however due to their nature I know they satisfy the conditions needed to apply the Gram-Schmidt algorithm. Before I embark on some R coding, I wanted to check that there is no facility / function already around that computes the orthogonalized set of vectors? I have performed

Dear all R users, Can anyone tell me to calculate Eigen value of any real symmetric matrix which algorithm R uses? Is it Jacobi method ? If not is it possible to get explicit algorithm for calculating it? Thanks and regards, Arun [[alternative HTML version deleted]]

Dear all, Can any one tell me how can i perform Orthogonal Polynomial Regression parameter estimation in R? -------------------------------------------- Here is an "Orthogonal Polynomial" Regression problem collected from Draper, Smith(1981), page 269. Note that only value of alpha0 (intercept term) and signs of each estimate match with the result obtained from coef(orth.fit). What

man for `qr` says that the function uses LINPACK's DQRDC, while it in fact uses DQRDC2. ``` The QR decomposition of the matrix as computed by LINPACK or LAPACK. The components in the returned value correspond directly to the values returned by DQRDC/DGEQP3/ZGEQP3 ```

Looking to the wonderful statistical advice that this group can offer. In behavioral science applications of stats, we are often introduced to coefficients for orthogonal polynomials that are nice integers. For instance, Kirk's experimental design book presents the following coefficients for p=4: Linear -3 -1 1 3 Quadratic 1 -1 -1 1 Cubic -1 3 -3 1 In R orthogonal

Dear Sirs: Please pardon me I am very new to R. I have been using MATLAB. I was wondering if R would allow me to do principal components analysis on a very large dataset. Specifically, our dataset has 68800 variables and around 6000 observations. Matlab gives "out of memory" errors. I have tried also doing princomp in pieces, but this does not seem to quite work for our approach.

On Fri, Feb 26, 2016 at 8:43 PM, Chandler Carruth <chandlerc at google.com> wrote: > On Fri, Feb 26, 2016 at 8:40 PM Xinliang David Li <xinliangli at gmail.com> > wrote: > >> On Fri, Feb 26, 2016 at 8:30 PM, Chandler Carruth <chandlerc at google.com> >> wrote: >> >>> On Fri, Feb 26, 2016 at 8:15 PM Hal Finkel <hfinkel at anl.gov> wrote:

It is not clear to me that one can. If the singular value decomposition of A is the triple product P d Q', then the singular value decomposition of A'A=S is Q d^2 Q'. The information about the orthonormal matrix P is lost, is it not? ********************************************************** Cliff Lunneborg, Professor Emeritus, Statistics & Psychology, University of Washington,

I'd like to fit linear models on very large datasets. My data frames are about 2000000 rows x 200 columns of doubles and I am using an 64 bit build of R. I've googled about this extensively and went over the "R Data Import/Export" guide. My primary issue is although my data represented in ascii form is 4Gb in size (therefore much smaller considered in binary), R consumes about

Dear all, I am fitting a multivariate linear model with 7 response variables and 1 explanatory variable. The following matrix P: P <- cbind( c(1,-1,0,0,0,0,0), c(2,2,2,2,2,-5,-5), c(1,0,0,-1,0,0,0), c(-2,-2,0,-2,2,2,2), c(-2,1,0,1,0,0,0), c(0,-1,0,1,0,0,0)) should consist of orthogonal elements (as can be shown using %*% on the individual columns). However, when I use

Hi. I have been looking at the PCAproj function in package pcaPP (R 2.4.1) for robust principal components, and I'm trying to interpret the results. I started with a data matrix of dimensions RxC (R is the number of rows / observations, C the number of columns / variables). PCAproj returns a list of class princomp, similar to the output of the function princomp. In a case where I can

----- Original Message ----- > From: "Xinliang David Li" <xinliangli at gmail.com> > To: "Chandler Carruth" <chandlerc at google.com> > Cc: "Hal Finkel" <hfinkel at anl.gov>, "llvm-dev" > <llvm-dev at lists.llvm.org>, "Philip Reames" > <listmail at philipreames.com>, "Duncan P. N. Exon Smith"

On Fri, Feb 26, 2016 at 8:57 PM, Hal Finkel <hfinkel at anl.gov> wrote: > > > ------------------------------ > > *From: *"Xinliang David Li" <xinliangli at gmail.com> > *To: *"Chandler Carruth" <chandlerc at google.com> > *Cc: *"Hal Finkel" <hfinkel at anl.gov>, "llvm-dev" <llvm-dev at lists.llvm.org>, >

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

Hey, all Will you please tell me how to generate multiple square orthogonal matrices for data transformation usage? Thanks. Fred

Dear List, I'm trying to draw vector in XYZ with rgl under use of a cylinder3d. Therefore I scale and rotate a basis-cylinder). However, somehow the rotation is wrong as verified by overplotting arrow3d(). Where is my mistake? library(heplots) library(rgl) # ... 2 vectors data=data.frame(row.names=c('X','Y','Z'), x1=c(2,1,5),y=c(4,3,2))