similar to: Bartlett's Test of Sphericity

Apologies for the obvious, but just to clarify: there is no reason to "justify" a PCA -- it's just an eigen decomposition of a matrix and is therefore "justified" by linear algebra. If one wants to determine whether some subset of the eigenvectors = principal components suffice to "represent" the data in some sense, then that is where distributional

Hello, I am working on extracting data from sound recording (fee bee song of the black-capped chickadee). I obtained a two column matrix with x=time(s) and y=frequency(khz). The part of the recording that is interesting me is when the frequency is stable. Does somebody ever used a fonction that extract only constant data among a huge list of data (sample= 180000)? Thanks Thibault Grava

Hello, I'm using R to run a acoustic analysis software called Seewave. I ask the code to extract a list of variables from my recording, and the program give ONE table for each of these. The tables consist of a two column data.frame with the time in column 1 and the frequency in column 2. However, for my purpose I need only one column with the time first and the frequency second. I tried

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,

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

[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

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

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

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

I'm an experimental psychologist and when I run ANOVA analysis in SPSS, I normally ask for a test of non-sphericity (Box's M-test). I also ask for output of the corrections for non-sphericity, such as Greenhouse-Geisser and Huhn-Feldt. These tests and correction factors are commonly used in the journals for experimental and other psychology reports. I have been switching from SPSS to R

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]]

I've read through many postings about principle component analysis in the R-help archives, but haven't been able to piece together the information I need. I'd like to recreate an SPSS-like experience of factor analysis using R. Here's what SPSS produces: 1. Scatterplots of all possible variable pairs, with regression lines. xyplot(my.dataframe) is perfect but for the lack of

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,

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

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

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 all, I would like to test the homoegeneity of variances between several linear model for some analysis of covariance. It seems that the Bartlett's test is a good test to use but I am having problem using with linear model and I cannot find any examples on the internet. There are some examples for comparisons of variances but not linear models. If I take the hellung data set, which is

Hi All, How do i calculate KMO for a dataset? *Dataset:---------------------* m1 m2 m3 m4 m5 m6 m7 m8 1 2 20 20 2 1 4 14 12 2 9 16 3 5 2 5 5 15 3 18 18 18 13 17 9 2 4 4 7 7 2 12 2 11 11 11 5 7 8 5 19 5 2 20 18 6 7 4 7 4 7 9 3 3 7 5 5 5 12 5 13 13 12 8 6 6 4 3 5 17 17 16 9 12 12 4 2 4 4 14 14 10 5 14

Dear colleagues, I've been searching for information on the Kaiser-Meyer-Olkin (KMO) Measure of Sampling Adequacy (MSA). This statistic is generated in SPSS and is often used to determine if a dataset is "appropriate" for factor analysis -- it's true utility seems quite low, but it seems to come up in stats classes a lot. It did in mine, and a glance through the R-help

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