similar to: Cumulative sum of vector

Hello I am using the following command but not able to text the values on the graph can someone please make suggestions for improvement #here is the command loc_mds <- cmdscale(dist.r, k = 7, eig = TRUE) loc_mds$eig sum(abs(loc_mds$eig[1:2]))/sum(abs(loc_mds$eig)) sum((loc_mds$eig[1:2])^2)/sum((loc_mds$eig)^2) x <-loc_mds$points[,1] y <-loc_mds$points[,2] plot(x, y,

GENERAL REFERENCE ON NONLINEAR OPTIMIZATION? What are your favorite references on nonlinear optimization? I like Bates and Watts (1988) Nonlinear Regression Analysis and Its Applications (Wiley), especially for its key insights regarding parameter effects vs. intrinsic curvature. Before I spent time and money on several of the refences cited on the help pages for "optim",

The following code in clusplot.default (package cluster) is in error: x1 <- cmdscale(x, k = 2, eig = TRUE) var.dec <- sum(x1$eig)/sum(diag(x1$x)) if (var.dec < 0) var.dec <- 0 if (var.dec > 1) var.dec <- 1 x1 <- x1$points x1 has components with names "points" and "eig", not "x", so

Hi all, I've been trying to figure out how to superimpose a histogram on a biplot that shows the relative contribution of each axis. I have been using the NIPALS function (http://biomserv.univ-lyon1.fr/~dray/ files/softwares/nipals.R) to run principal component analyses. Here is a toy example. source("http://biomserv.univ-lyon1.fr/~dray/files/softwares/nipals.R")

Hi I need to extract the data returned by Eigen to plot the eigenvectors. However, when I try and eigv = eigen(covariance); it returns an object with the matrices containing eigenvalues and vectors.. how can I extract the eigenvector matrix from this?? When I try mat = eig["vectors"] it returns a matrix with the "$vectors" string on top , how can I remove this? code: > eig

Dear R-listers Is there anyone who knows why I get different eigenvectors when I run MatLab and R? I run both programs in Windows Me. Can I make R to produce the same vectors as MatLab? #R Matrix PA9900<-c(11/24 ,10/53 ,0/1 ,0/1 ,29/43 ,1/24 ,27/53 ,0/1 ,0/1 ,13/43 ,14/24 ,178/53 ,146/244 ,17/23 ,15/43 ,2/24 ,4/53 ,0/1 ,2/23 ,2/43 ,4/24 ,58/53 ,26/244 ,0/1 ,5/43) #R-syntax

Folks: I'm trying to port some code from python over to R, and I'm running into a wall finding R code that can solve a generalized eigenvalue problem following this function model: http://docs.scipy.org/doc/scipy/reference/generated/scipy.linalg.eig.html Any ideas? I don't want to call python from within R for various reasons, I'd prefer a "native" R solution if one

Dear all, I want to compute the eigenvalues of a complex matrix for some statistics. Comparing it to its matlab/octave sibling, I don't get the same eigenvalues in R computing it from the exact same matrix. In R, I used eigen() and arpack() that give different eigenvalues. In matlab/octave I used eig() and eigs() that give out the same eigenvalues but different to the R ones. For real

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

Dear R-helpers, I have two data frames, op and em4: > str(op) 'data.frame': 37 obs. of 5 variables: $ m : num 0.202 0.336 0.122 0.139 0.14 ... $ lln : num 0.798 0.643 0.863 0.835 0.823 ... $ rrn : num 0.789 0.702 0.894 0.895 0.923 ... $ asym2: num 0.177 0.304 0.108 0.187 0.274 ... $ asym3: num 0.0755 0.0975 0.0818 0.0651 0.13 ... > str(rownames(op)) chr

I have a covariance matrix that is not positive semi-definite matrix and I need it to be via some sort of adjustment. Is there any R routine or package to help me do this? Thanks, Roger [[alternative HTML version deleted]]

By any chance is anyone aware of an R function that duplicates Octave's poly function? Here is a description of Octave's poly function: Function File: poly (A) If A is a square N-by-N matrix, `poly (A)' is the row vector of the coefficients of `det (z * eye (N) - a)', the characteristic polynomial of A. As an example we can use this to find the eigenvalues

Hi, I am trying to parallel computing with foreach function, but not able to get the result. I know that in parallel processing, all result is collected in list format, but I am not able to get input there. Any help is really appreciated. esf.m <-foreach (i = 1:n.s, .combine=rbind) %dopar% { EV <- as.data.frame(eig$vectors[,1:n.candid[i]]) colnames(EV) <- paste("EV",

Dear R developers, there appears to be a small problem with function cmdscale: for non-Euclidean distance matrices, using option add=FALSE (the default), cmdscale misses the smallest eigenvalue. This affects GOF statistic g.1 (See Mardia, Kent + Bibby (1979): Multivariate Analysis, eq. (14.4.7). The corresponding formula in Cox + Cox (2001): Multidimensional Scaling, 2nd ed., p 38, would

Hello, I have a dilemma that I'm hoping the R gurus will be able to help resolve. For background: My data is in the form of a (dis)similarity matrix created from taking the inverse of normalized reaction times. That is, each cell of the matrix represents how long it took to distinguish two stimuli from one another-- a square matrix of 45X45 where the diagonal values are all zero (since this

Dear All, I was wondering whether any one knows of a matrix bending function in R that can turn non-positive definite matrices into the nearest positive definite matrix. I was hoping there would be something akin to John Henshall's flbend program (http://agbu.une.edu.au/~kmeyer/pdmatrix.html), which allows the standard errors of the estimated matrix elements to be considered in the

Hello can anyone help, I have been running the following script to obtain a PCA plot but the end result is rather disappointing as the points are very very small and there are no titles etc geochemdata<-read.csv(file.choose(),header=TRUE) names(geochemdata) library(vegan) bstick<-function(n, tot.var=1) rev(cumsum(tot.var/n:1)/n) geopca<-rda(geochemdata, scale=TRUE) geopca

Hi all: How can I calculate the cumulative variance (or variance for each component) in correspondence analysis? If were possible in ADE4 package Thank you -- Antonio Punzón Merino O__---- Instituto Español de Oceanografía c/ /'_ --- Centro Oceanográfico de Santander (*) \(*) -- Promontorio de San Martín S/N ~~~~~~~~~~ 39004-Santander; Spain PO BOX: 240 Tlf: +34 942 29 10 60 Fax: +34

Hello, We are trying to find a function to compute "stress" in our multidimensional scaling analysis of a dissimilarity matrix. We've used "dist()" to create the matrix and "cmdscale()" for the scaling. In order to determine the number of dimensions we would like to plot stress vs. dimensions. However, we cannot find a pre-made command. It seems that other

The following problem occurs in R 1.4.0 and 1.3.1 for Windows95, but not in R 1.2.0 for Windows95. The problem does not occur in R 1.4.0 for Linux PC, Linux Alpha and HP-UX. Sometimes, the type of 'Size' of an object of type 'dist' changes from integer into double. Running cmdscale on such a 'dist' object gives invalid results. I don't know what should be considered