similar to: how to test for equality of covariance Matrices in lda

I have conducted a discriminant function analysis with lda() in the MASS Package, and I am interested in testing that the covariance matrices of the groups are equal. Does anybody have any suggestions on how I could test for equality between covariance matrices? Any help would be great. Thank you in advance. Cheers -Rob -- [[alternative HTML version deleted]]

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

Hi list, I'm looking about lda function. I'd like to know how calcolate the value of the discriminant functions for the original datas. I see that in the result object "lda" there is $scaling a matrix which transforms observations to discriminant functions, normalized so that within groups covariance matrix is spherical. I'd like to have the value of the discriminant

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

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

Dear Group Members, Forgive me if I am a little bit out of subject. I am looking for a good way to test the homogeneity of two variance-covariance matrices using R, prior to a Hotelling T test. Youll probably tell me that it is better to use a robust version of T, but I have no precise idea of the statistical behaviour of my variables, because they are parameters from the harmonics of

Hello all- I am a relatively new user of R and am working through a graduate course in Statistics that uses Minitab, SAS and some Matlab. I like using R but am having some trouble lining up the output from lda() to that of the other programs' results. The dataset below is a modified set of wine data from the Pinot Noir data set as an illustration of the 2 group LDA scenario. Mo Ba

-- Dear R users, I have data on around 2000 birds from 3 generations for which I know an individual's pedigree (i.e. the relationship it shares with other individuals e.g brother, uncle, mother) and also a pedigree based on foster-families, because half broods were removed from their nest of origin and placed in a foster parent's nest. From this I want to model two types of random

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

I am having trouble getting a covariance matrix for each cluster which is output by kmeans(). My input looks like: > imagedat <- read.table("table", header=TRUE) > imagedat Red Green Blue 0_0 5 7 8 1_0 5 5 18 2_0 7 8 49 3_0 22 8 76 4_0 54 10 67 5_0 50 9 28 6_0 18 10 15 7_0 9 7 6

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-helpers, if I am right a discriminant analysis can be done with "lda". My questions are: 1. What method to discriminate the groups is used by "lda" (Fisher's linar discriminant function, diagonal linear discriminant analysis, likelihood ratio discriminant rule, ...)? 2. How can I see, which method is used? (Typing just lda does not give me any code). Thank you in

Dear friends, After fitting a generalized linear models ,i hope to get the variance of alpha,variance of beta and their covariance, that is , the variance-covariance matrix/information of alpha and beta , suppose *B* is the object of GLMs, i use attributes(B) to look for the options ,but can't find it, anybody knows how to get it? > attributes(B) $names [1] "coefficients"

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

Fellow R Users: I'm not extremely familiar with lda or R programming, but a recent editorial review of a manuscript submission has prompted a crash cousre. I am on this forum hoping I could solicit some much needed advice for deriving a classification equation. I have used three basic measurements in lda to predict two groups: male and female. I have a working model, low Wilk's lambda,

Hello R-users, Does anyone know how to customize a corStruct object to be used in gls? I would either like to create the covariance matrix from scratch, or alter the diagonal elements of an existing corStruct object and pass that to gls. Any ideas would be appreciated!

For an application of ridge regression, I need to get the covariance matrices of the estimated regression coefficients in addition to the coefficients for all values of the ridge contstant, lambda. I've studied the code in MASS:::lm.ridge, but don't see how to do this because the code is vectorized using one svd calculation. The relevant lines from lm.ridge, using X, Y are:

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 there, i have one more question about LDA. just to make surei understand, suppose we have two classes, then if i specify a prior=c(.3,.7) in lda(...) this will affect my between classes covariance matrix as in: SB = (.3*m1 - .7*m2) %*% inv(Sigma) %*% t(.3*m1 - .7*m2) [is Sigma affected ?] and the threshold to decide which class to assign 'test' data = log(.3/.7) if i specify a

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: