similar to: How to do Hotelling's t2 test?

I want to calculate the probability that a group will include a particular point using the squared Mahalanobis distance to the centroid. I understand that the squared Mahalanobis distance is distributed as chi-squared but that for a small number of random samples from a multivariate normal population the Hotellings T2 (T squared) distribution should be used. I cannot find a function for

I am doing a cluster analysis with hclust. I want to get hclust to output the Hotelling's T squared statistic for each cluster so I can evaluate is data points should be in a cluster or not. My research to answer this question has been unsuccessful. Does anyone know how to get hclust to output the Hotelling's T squared statistic for each cluster? Mike [[alternative HTML version

Hi all, I've got the following matrix : ? mat <- matrix(rnorm(700), ncol=5, dimnames=list( paste("f", c(1:140), sep="_"), c("A", "B", "C", "D", "E"))) I can see that currently most of the multivariate Hotelling T2 tests are limited for application on two groups/samples. I wud appreciate if someone can provide me a

Hi! I tested two different implementations of the robust MCD estimator: cov.mcd from the MASS package and covMcd from the rrcov package. Tests were done on the hbk dataset included in the rrcov package. Unfortunately I get quite differing results -- so the question is whether this differences are justified or an error on my side or a bug? Here is, what I did: > require(MASS) >

This is my first post requesting help to this mailing list. I am new to R. My apologies for any breach in posting etiquette. I am new to this language and just learning my way around. I am attempting to run some sample code and and am confused by the error message: Loading required package: rrcov Error in fitNorm2(fdat[, "FSC-H"], fdat[, "SSC-H"], scalefac = ScaleFactor) :

Hi I have a couple of functions that work on the object created by another R command and then print out or summarise the results of this work. The main function is defined as: hotelling.t <- function(obj) { #internal commands } I then have print.hotelling.t() that takes the list returned by hotelling.t and prints it with some extra significance calculations, formatting, etc. I want to

Hello there, I have two data sets with 14 variables each, and wish to do the test for equality of their covariance matrices and mean vectors. Normally these tests should be done by chi square test (box provided) and Hotelling's T square test respectively. Which R functions could do this kind of test? I just find some functions could do for one dimension, but no for multidimension. Some one

Dear All It is very kind of you to guide me. When I want to run this line, I see this error stat.obs <- apply(GS, 2, function(z) Hott2(t(DATA[which(z==1),]), cl)) Error in colSums(w * x) : 'x' must be an array of at least two dimensions cl <- as.factor(y) GS: a matrix with 0 or 1 GS: gene sets -> a data matrix with rows=genes, columns= gene sets, GS[i,j]=1 if gene i in

Hello, I'm using the R-2.13.1 version in Windows and I'm trying to do a robust Pca with the following: x<-matrix(0.5,30,30) library("rrcov") y<-PcaCov(x) The following error occurs: Error: diff(sv)<0 ist not all TRUE The same error occurs with the other functions. What does this mean and how can I perform the robust PCA with these functions by using a quadratic

When I install an R package with cpp codes such as rrcov via CRAN (under R 3.1.1, using no Makevars file and under Ubuntu 14.04 using GCC 4.8), the cpp code is compiled with the -o3 flag (in fact, looking at the Makeconf file this seem to again be the default since R 3.1.1) But when I install my own package via CRAN it is compiled with the -o2 flag. My questions are what is causing my

[Env: R 2.14.2 / Win Xp] In the script below, I want to select some variables from rrcov::OsloTransect, delete cases with any missing data, and subset the data frame Oslo to remove cases for two levels of the factor litho that occur with low frequency. The checks I run on my new data frame Oslo look OK, but I when I try to fit a multivariate linear model with lm(), I am getting an error:

By accident, I left out the lines defining n1 and n2. Here it is as a function. Peter B. hotelling <- function(d1,d2){ k <- ncol(d1) n1 <- nrow(d1) n2 <- nrow(d2) xbar1 <- apply(d1,2,mean) xbar2 <- apply(d2,2,mean) dbar <- xbar2-xbar1 v <- ((n1-1)*var(d1)+(n2-1)*var(d2))/(n1+n2-2) t2 <- n1*n2*dbar%*%solve(v)%*%dbar/(n1+n2) f <-

Hi, I am a graduate student at Stanford University and I have a general statistics question. What exactly is the difference between doing a two-factor repeated measures ANOVA and a Hotelling T-squared test for a paired comparison of mean vectors? Given: Anova: repeated measures on both factors, 1st factor = two different treatments, 2nd factor = 4 time points, where you are measuring the blood

Dear all, The other day I was reading this post [1] that slightly surprised me: "To reject the null of no correlation, an hypothsis test based on the normal distribution. If normality is not the base assumption your working from then p-values, significance tests and conf. intervals dont mean much (the value of the coefficient is not reliable) " (BOB SAMOHYL). To me this implied that in

Good morning I've made an PCA and I'd like to plot a confidence region based on Hotelling T2? Does anyone know how to compute it? Thank you -- View this message in context: http://r.789695.n4.nabble.com/hotelling-and-confidence-region-tp3524204p3524204.html Sent from the R help mailing list archive at Nabble.com.

# Peter B. Mandeville kindly offered to send me code for Hotelling's T^2 # Test. Unfortunately there seems to be no route to his machine. # So i'm trying to reach him via the Mailing List. ------------------------------------------------------------------------ Sir, this morning i recieved your message about the availability of the code for Hotelling's Test. I hurried to find out

Hi everybody ... I have to perform Hotteling's T^2 Test - more generally a MANOVA - on a set of data. Is there a (simple?) possibility to do it in R? The somewhat obvious way to do it would be > summary(aov(Y ~ x1 + x2 ... + xn)) where Y would be a two-column matrix. But this does not work in any possible combination of matrix or factor dimensions! Is it principally not possible

I haven't seen this cryptic warning before: > update.views('Robust') Warning message: In update.views("Robust") : The following packages are not available: covRobust, distr, FRB, MASS, mblm, multinomRob, mvoutlier, quantreg, RandVar, rgam, RobAStBase, robfilter, RobLox, RobRex, robust, RobustAFT, robustbase, ROptEst, ROptRegTS, rrcov, sandwich, wle >

Hello everyone. In my last post I did not explained my problem quite well. I made a principal component analysis and took the 2 first principal components. I made ​​a chart of my points based on the score of the 2 PC. I would like to add on this graph a 95% confidence region. To do this I used the ellipse function as follows: pcsref=PC$score[data[,1]==ref,1:2] #matrix containing the scores

Hi, I have a strange package installation problem after update to R 2.11.0 on Fedora Core 12. A colleague of mine with the very same Fedora and R versions doesn't have this problem, while logging on to his computer but installing from my settings and into my local library path also shows the problem, so it seems to be related to some of my environment settings. Installation of several