similar to: Equality between covariance matrices?

when using the two-group discriminant analysis,we need to test for equality of covariance Matrices in lda.as whenm we formed our estimate of the within-group covariance matrix by pooling across groups,we implicitly assumed that the covariance structure was the same across groups.so it seems important the test the equality.but i can not find function in R to do these.

Hello R list subscribers, I am trying to use the "by" command to create line-specific variance covariance matrices (where "x" is the original data matrix): by(x, x$line, function(d) { d.clean <- d[,-1]}) write.table(d.clean$line[1,1], sep = ",", file = "covariances.csv", col.names = FALSE, row.names = FALSE, append = TRUE) write.table("", sep

Dear R-mailers, I would like to test for equality of population covariance matrices. But I can't find a R tool to do so. I saw, that other people had the same question, but I could not find an answer to it, I would appreciate to know the missed link. Thank you, b.w. K. Steinmann

Hello, I am a new R user and trying to learn how to implement the mahalanobis function to measure the distance between to 2 population centroids. I have used STATISTICA to calculate these differences, but was hoping to learn to do the analysis in R. I have implemented the code as below, but my results are very different from that of STATISTICA, and I believe I may not have interpreted the help

Hi Now a more theoretical question. I have two correlation matrices - one of a set of variables under a particular condition, the other of the same set of variables under a different condition. Is there a statistical test I can use to see if these correlation matrices are "different"? Thanks Mick

List members, Via searches I've seen similar discussion of this topic but have not seen resolution of the particular issue I am experiencing. If my search on this topic failed, I apologize for the redundancy. I am attempting to generate random covariance matrices but would like the corresponding correlations to be uniformly distributed between -1 and 1. The approach I have been using is:

Hi, I've been trying to figure out a special set of covariance matrices that causes some symmetric zero elements in the inverse covariance matrix but am having trouble figuring out if that is possible. Say, for example, matrix a is a 4x4 covariance matrix with equal variance and zero covariance elements, i.e. [,1] [,2] [,3] [,4] [1,] 4 0 0 0 [2,] 0 4

I am in a discriminant analysis situation with a frame containing several variables and a grouping factor, if you like: set.seed(200906) exampledf <- as.data.frame(matrix(rnorm(50,5,2),nrow=10,ncol=5)) exampledf$Group <- factor(rep(c(1,2,3),c(3,3,4))) exampledf I'm sure there must be a simple way to get the within group pooled covariance matrix but I haven't found it yet. I

I am creating covariance matrices from sets of points, and I am having frequent problems where I create matrices that are non-positive definite. I've started using the corpcor package, which was specifically designed to address these types of problems. It has solved many of my problems, but I still have one left. One of the matrices I need to calculate is a cross-covariance matrix. In other

While I don't know anything about Box's M test, I googled around and found a Matlab M-file that computes it. Below is my straight-forward translation of the code, without knowing Matlab or the formula (and done in a few minutes). I hope this demonstrates one of Prof. Ripley's point: If you really want to shoot yourself in the foot, you can probably program R to do that for you. [BTW:

Hallo! I want to understand / recalculate what is done to get the CI of the logistic regression evaluated with lrm. As far as I came back, my problem is the variance-covariance matrix fit$var of the fit (fit<-lrm(...), fit$var). Here what I found and where I stucked: ----------------- library(Design) # data D<-c(rep("a", 20), rep("b", 20)) V<-0.25*(1:40) V[1]<-25

Dear all R users, I wanted to calculated a sample Variance covariance matrix of a five-variate normal distribution. However I stuck to calculate each element of that matrix. My question is should I calculate ordinary variance and covariances, taking pairwise variables? or I should take partial covariance between any two variables, keeping other fixed. In my decent opinion is I should go for the

Hello all, I'm trying to create covariance matrices that, when processed via CFA methods (in the sem package) will produce exact fit with simple structure down to poor fit with cross loadings. What is the best way to do this? I don't really need to have the exact loop code, but maybe an explanation of how to make a few of the matrices or an explanation of the rationale behind performing

I would appreciate confirmation that the function vcov(model.nlme) gives the var-cov matrix of the fixed effects in an nlme model. Presumably the random-effects var-cov matrix is given by cov(ranef (model.nlme)? Rob Forsyth

Hello, are there any good methods in R that will test if two correlation matrices (obtained in different ways) are equal? Something better than the Mantel test would be preferable. Regards, Patrik Waldmann -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info",

Hello- I'm trying to do some repeated measures ANOVAs. In the past, using SAS, I have used the framework outlined in Littell et al.'s "SAS System for Mixed Models", using the REPEATED statement in PROC MIXED to model variation across time within an experimental unit. SAS allows you to specify different within-unit covariance structures (e.g., compound symmetric, AR(1), etc.) to

Dear all, How do I get the standardized covariance (the correlation) between two latent variables? 'standardized.coefficients' gives standardized path coefficients, but not covariances. The covariance estimates are easily obtained from fit$coeff or 'summary', but EQS reports both the covariance and the correlation, how can I get that? best wishes, Mike

This is a covariance calculation question so nothing to do with R but maybe someone could help me anyway. Suppose, I have two random variables X and Y whose means are both known to be zero and I want to get an estimate of their covariance. I have n sample pairs (X1,Y1) (X2,Y2) . . . . . (Xn,Yn) , so that the covariance estimate is clearly 1/n *(sum from i = 1 to n of ( X_i*Y_i) ) But,

Hi, I want to simulate a data set with similar covariance structure as my observed data, and have calculated a covariance matrix (dimensions 8368*8368). So far I've tried two approaches to simulating data: rmvnorm from the mvtnorm package, and by using the Cholesky decomposition (http://www.cerebralmastication.com/2010/09/cholesk-post-on-correlated-random-normal-generation/). The problem is

I have a bayesian hierarchical normal regression model, in which the regression coefficients are nested, which I've wrapped into one regression framework, y = X %*% beta + e . I would like to run data through the model in a filter style (kalman filterish), updating regression coefficients at each step new data can be gathered. After the first filter step, I will need to be able to feed