search for: spherical

I believe that the following function is a good function for eyeballing sphericity. But, I can't find functions for tests anywhere (g-g or h-f). Anyone who wants to can feel free to use it. It is bad that R does not provide any tests for sphericity AND cannot do a MANOVA on repeated measures (although lme is an alternative add on package). One of these solutions must be provided in the

...t a theoretical one. I have read that use of k-means clustering assumes sphericity of data distribution. Can anyone explain me what this means? My statistical background is too poor. Is it another kind of distribution, like gaussian or binomial? What does it happen if the distribution is not spherical? Could you give me an example or a link to information about this? Thanks for your help David

I have 3 questions (below). Background: I am teaching an introductory statistics course in which we are covering (among other things) repeated measures anova. This time around teaching it, we are using R for all of our computations. We are starting by covering the univariate approach to repeated measures anova. Doing a basic repeated measures anova (univariate approach) using aov() seems

I haven't been able to find an answer on this that's direct, only implied. In several places I have read that when people asked for sphericity tests they were guided toward lme or mlm models. But, there is no direct indication that the lme method is not subject to the sphericity assumption. In fact, it seems like it should be. Its just a linear model that handles random and

...;- cbind(x0,x2,x4) # n is the number of 'subjects' or rows; while # k is the number of levels in the factor (columns), # which is the number of repeated measures n <- dim(data)[1] k <- dim(data)[2] # if k <= 2, the epsilon correction is not required. # When the data are perfectly spherical, epsilon = 1. # The minimum value possible for epsilon is epsi = 1 / (k - 1) # if epsi == 1 and k == 2, quit now... # --- # calculate variance-covariance matrix # diagonal entries are variance, # off-diagonal are covariance S <- var(data) # --- # Now estimate Epsilon D <- k^2 * ( mean(di...

[corrected dataset below] Hello everyone, I am trying to do within subjects repeated measures anova followed by the test of sphericity (sample dataset below). I am able to get either mixed model or linear model anova and TukeyHSD, but have no luck with Repeated-Measures Assuming Sphericity or Separate Sphericity Tests. I am trying to follow example from "car" package, but it seems

Hello Dear R user, I want to conduct a Principal components analysis and I need to run two tests to check whether I can do it or not. I found how to run the KMO test, however i cannot find an R fonction for the Bartlett's test of sphericity. Does somebody know if it exists? Thanks for your help! Thibault [[alternative HTML version deleted]]

Hello everyone, I am trying to do within subjects repeated measures anova followed by the test of sphericity (sample dataset below). I am able to get either mixed model or linear model anova and TukeyHSD, but have no luck with Repeated-Measures Assuming Sphericity or Separate Sphericity Tests. I am trying to follow example from "car" package, but it seems that I am not getting something

Hi, Is there a way to do a sphericity test in R for repeated measures ANOVA (using aov or lme)? I can't find anything about it in the help. Thanks Orou [[alternative HTML version deleted]]

Hi R-users Does anybody know if there are functions or packages in R for sphericity tests like Bartlett's or Mauchly's? Thanks! __________________________________________________________ Eng. Agr., M.Sc. Eduardo Dutra de Armas __________________________________________________________ Centro de Energia Nuclear na Agricultura (CENA/USP) Laboratório de Ecotoxicologia Av.Centenário 303,

Dear Prof. Baron, thankyou for your pdf paper on using R, at http://www.psych.upenn.edu/~baron/rpsych.pdf I am currently interested in the sphericity notes on pp. 45-47. The equation on p. 46 suggests the following R calculation: D <- k^2 * ( mean(diag(S)) - mean(S) )^2 while the notes in the pdf indicate the following: D <- k^2 * ( mean(S) - mean(diag(S)) )^2 Can you please consider

Hello and thanks in advance I am running a glm in R the code is as follows with residual diagnostic code below model4<-glm(Biomass~(Treatment+Time+Site)^2, data=bobB, family=quasi(link="log", variance="mu")) par(mfrow=c(2,2)) plot(model2) to test the effect of grazing exclusion of feral horses for a Phd with following factors: Treatment - 3 levels which are grazed

With respect to calculating the epsilon index of sphericity for ANOVA, discussed on pp. 45-47 of: http://www.psych.upenn.edu/~baron/rpsych.pdf It notes that epsilon is not required for a repeated measures design with only k=2 levels, as the minimum value of epsilon (e) is given by: e = 1/(k-1) so for k=2, we have e = 1 (ie, no correction of the F test df; see p. 46). These notes apply to a

Hi, I'm getting to grips with using R to perform repeat measures analysis of variance and was wondering is there a potted method to obtain Huynh and Feldt or other adjustments for sphericity violations? Thanks Dominic Dr Dominic J Barraclough Dept Neurobiology and Anatomy, University of Rochester, 601 Elmwood Av, Box 603, Rochester NY 14642 tel (716) 2753627 dominic_barraclough at

Hey, Is this right for a sphericity test? x <- mymatrix mycov <- cov(x) bartlett.text (array (mycov), myconditions)

Does anyone have any suggestions for how to test for violation of the sphericity assumption and apply the Huynh-Feldt correction to a repeated-measure model? My model follows the format below: Rep.anova=aov(Y~X1*X2+Error(Subject/(X1*X2),data=DataFile)) I've already looked at the explanations aov() and Error() in Baron's “Notes on the use of R for psychology experiments and

Hello to all, I am trying to use the spherical wavelets package on my altimetry data. I am having some problems now and then. Currently I am having an error of "error in matrix(0,KK,JJ) : too many elements specified" there is a thread of possibly same error (https://stat.ethz.ch/pipermail/r-help/2005-January/064308.html) but...

Is there a function that calculates the power of a repeated-measure ANOVA design, e.g., 2 groups, 4 within-subject factors, an average 0.40 correlation between the 4 within factors, etc. I don't think I can use power.anova.test() because it does not consider corr=0.40. I am hoping that someone has already implemented the method by Muller & Barton (1989, JASA 84: 549-555) on how to

Dear helpers, I have a 2x2 mixed design with two "groups" (between-subjects) and two presentation-types (within-subjects). The difference between groups is in the order of manipulations: group.CD having first a block of present.type.C and then a block of present.type.D, each block containing 31 trials. group.DC having first a block of present.type.D and then a block of present.type.C,

...ll phone: 510-301-0179 Email: matzke at berkeley.edu Mailing address: Department of Integrative Biology 3060 VLSB #3140 Berkeley, CA 94720-3140 ----------------------------------------------------- "[W]hen people thought the earth was flat, they were wrong. When people thought the earth was spherical, they were wrong. But if you think that thinking the earth is spherical is just as wrong as thinking the earth is flat, then your view is wronger than both of them put together." Isaac Asimov (1989). "The Relativity of Wrong." The Skeptical Inquirer, 14(1), 35-44. Fall 1989. htt...