similar to: Finding the Principal components

On Thu, 12 Apr 2007, Fernando Magno Quintao Pereira wrote: >> I'm definitely interested in improving coalescing and it sounds like >> this would fall under that work. Do you have references to papers >> that talk about the various algorithms? > > Some suggestions: > > @InProceedings{Budimlic02, > AUTHOR = {Zoran Budimlic and Keith D. Cooper and Timothy

hey, my function doesn?t work. can somebody help me? the graphic doesn?t work and also the function. thnx a lot. N=10 n=100 p_0=c(1/5,1-1/5) power = function(p,m) { set.seed(1000) H=matrix(0,nrow=N,ncol=1) for(i in 1:N) { x <- matrix(rnorm(n, 0, 0.5), ncol = m) y <- matrix(rnorm(n, 0, 0.8), ncol = m) l <- diag(cor(x, y)) q_1 = qnorm(0.05, 0, 0.05) q_2 = qnorm(1 - 0.05, 0, 0.05)

hi how can i plot now this function??? have to be m= 2??? because of the dimensions?thanks for ur help myfun <- function(n, m, alpha = .05, seeder = 1000) { set.seed(seeder) x <- matrix(rnorm(n, 0, 0.5), ncol = m) y <- matrix(rnorm(n, 0, 0.8), ncol = m) l <- diag(cor(x, y)) cat("Correlations between two random variables \n", l, fill = TRUE) gute

hey, how can i plot this function??? thanks for ur help n=1000 m=2 k=n/m N=100 myfun <- function(n, m, alpha = .05, seeder = 1000) { l=matrix(0,nrow=m,ncol=N) for(i in 1:N){ set.seed(i) for(j in 1:m){ x=rnorm(n,0,0.5) y=rnorm(n,0,0.8) l[j,i]=cor((x[(((j-1)*k)+1):(((j-1)*k)+k)]), (y[(((j-1)*k)+1):(((j-1)*k)+k)])) } } for(i in 1:N){ for (j in 1:m){ gute <- function() { q_1 <-

I'm a programmer, not a mathmatician. I heard about R, and I'm wondering if anyone can tell me if there is an existing R function that can help with a problem we're currently trying to find an algorithm for. If R is not the answer, but you can recommend a known algorithm, that would help a lot! I'm on a project in a retail corporation, working on a program to assist retail buyers

Hi, We are developing a bounded model checker for C/C++ programs (http://baldur.iti.kit.edu/llbmc/) that operates on LLVM's intermediate representation. While checking a C++ program that uses STL containers we noticed that llvm-gcc and clang handle pointer differences in disagreeing ways. Consider the following C function: int f(int *p, int *q) { return q - p; } Here's the

Hi Stephan, > We are developing a bounded model checker for C/C++ programs > (http://baldur.iti.kit.edu/llbmc/) that operates on LLVM's intermediate > representation. While checking a C++ program that uses STL containers > we noticed that llvm-gcc and clang handle pointer differences in > disagreeing ways. > > Consider the following C function: > int f(int *p, int *q)

May be a simple question, but not understanding why in princomp I get different results for loadings and summary for my eigenvectors and eigenvalues. When I use pc.cr$loadings using the USArrests dataset the proportion of variance is equal for each of the components, but when summary(pc.cr) is used the proportion of variance is showing different proportions. My question is why do they differ? I

I have been playing around with sparse matrices in the Matrix package, in particularly with the Cholesky factorization of matrices of class dsCMatrix. And BTW, what a fantastic package. My problem is that I have to carry out repeated Cholesky factorization of a spares symmetric matrices, say Q_1, Q_2, ...,Q_n, where the Q's have the same non-zero pattern. I know in this case one does

Full_Name: Sahotra Sarkar Version: 2.2.0 OS: Windows XP Professional Submission from: (NULL) (146.6.130.180) The following two commands should give the same results for the eigenvectors but do not (there is a sign reversal for the first one): > summary(princomp(bumpus),loading = TRUE) Importance of components: Comp.1 Comp.2 Comp.3 Comp.4 Comp.5

Dear listserv, I am trying to perform a principal components analysis and create an output table of the eigenvalues for the dependent variables. What I want is to see which variables are driving each principal components axis, so I can make statements like, "PC1 mostly refers to seed size" or something like that. For instance, if I try the example from ?prcomp > prcomp(USArrests,

Hi all, I am attempting to run PCA on a matrix (nrow=66, ncol=84) using 'prcomp' (stats package). My data (referred to as 'Q' in the code below) are separate river streamflow gaging stations (columns) and peak instantaneous discharge (rows). I am attempting to use PCA to identify regions of that vary together. I am entering the following command:

Dear R users, I have been doing rotation on Principal components analyse, with varimax function and promax. Following those changes, I cannot find now how to get the spatial evolution and the variance. Indeed, with the function princomp that has been used to get the principal components, arguments such as $scores and $sdev were available to get the spatial evolution and the variance, with

Hello R-ers, I'm trying to do a weighted principal components analysis. I couldn't find any such option with princomp or prcomp. Does anyone know of a package or way to do this? More specifically, the observations I'm working with are averages from populations of varying sizes. I thus need to weight the observations by sample size. Ideally I could apply these weights at the cell

Hi, I am interested in using functional principal component analysis (Functional PCA). However, in the help of fda package, it does not have an example of pca.fd function. Does anyone have the example of pca.fd function? Thank you very much. Sincerely yours, Nantachai

HI all, I am trying to compute the EOF of a matrix using prcomp but unable to get the expansion co-efficients. is it possible using prcomp or are there any other methods thanks nuncio -- Nuncio.M Research Scientist National Center for Antarctic and Ocean research Head land Sada Vasco da Gamma Goa-403804 [[alternative HTML version deleted]]

Dear all, I have a dataset representing several geographical points (locations), each one having a specific value after performing a PCA. Now I'm trying to understand how to separately represent on a geographic map (of Europe) the pattern of PC1,2, ecc.with colors (e.g.heatmap) I have to add that the dataset includes relatively few points. Therefore, I would also like to interpolate the values

Hello I'm writing an example PCA analysis for some students. I've done PCA on a 2-column matrix to show it at the most simple form (a rotation of two axes). I can't, however, figure out how to plot the rotated axes as lines over top of the original data plotted on the x-y. Does anyone know how to do this? It will be a good way to show how the rotated axes describing the most

Dear All, It might be a simple question. But I could not find the answer from function “prcomp” or “princomp”. Does anyone know what are the codes to get coefficient and scores of Principal component analysis in R? Your reply will be appreciated! Best Zunqiu [[alternative HTML version deleted]]

Hello, I have a matrix with 267 columns, all rows of which have at least one column missing (NA). All three methods i've tried (pcs, princomp, and prcomp) fail with either "Error in svd(zsmall) : infinite or missing values in 'x'" (latter two) or "Error in cov.wt(z) : 'x' must contain finite values only" The last one happens because of the check if