similar to: Levene test of homogeneity of variance

Dear list members, Where can I find code for computing the p*p variance-covariance matrix given a vector of p variances (ordered varA, varB, ..., varp) and a vector of all possible correlations (ordered corAB, corAC, ..., corp-1,p)? I know that the covariance between 2 variables is equal to the product of their correlation and their standard deviations: corAB * varA^.5 * varB^.5 and so:

Dear all, I often import data sets from other programs that contain user defined missing values. As an example consider a variable MXPLOCO with a user defined missing value of as -9. > summary(MXPLOCO) Min. 1st Qu. Median Mean 3rd Qu. Max. NA's -9.000 4.750 5.000 5.349 6.000 8.500 2.000 The following command successfully converts the value -9 to NA. However,

Dear list members, How can I replace the nested for loops at then end of the script below with more efficient code? # Begin script__________________________________________________ # Dichotomous scores for 100 respondents on 3 items with # probabilities of a correct response = .6, .4, and .7, # respectively x1 <- rbinom(100,1,.6) x2 <- rbinom(100,1,.4) x3 <- rbinom(100,1,.7) #

Dear R community I am using the gee package to run logistics regression on paired cases from a panel sample. We are getting request from a reviewer to use sample weights to account for different level of attrition. After searching the documentation I am unable to find a way to incorporate sample weights into the gee formula. I found a way to incorporate precision weight but I understand that I

On Sun April 19, you posted the following on R-help: > After installing 2.9.0 I tried loading packages, but keep getting the following error. > > package 'robustbase' successfully unpacked and MD5 sums checked > Error in normalizePath(path) : > path[1]="C:\Program Files\R\R-2.9.0\library/robustbase": The system cannot find the file specified > I have had the

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Hi, All! To calculate Levene's Test for Homogeneity of Variance I use R Commander, and this is the output: > levene.test(Dataset$age, Dataset$sex) Levene's Test for Homogeneity of Variance Df F value Pr(>F) group 1 0.8739 0.3567 33 I am not sure what means "Pr(>F)"? Can anyone explain/translate this? Regards, Iurie Malai Department of Psychology and

I am trying to use Levene's test (of package car), but I do not understand quite well how to use it. '?levene.test' does not unfortunately provide any example. My data are in a data frame and correspond to 1 factor plus response. Could someone please give me an example about how to use the command levene.test(y, group) Thanks in advance, marta -- View this message in

> From: Peter Dalgaard BSA <p.dalgaard at biostat.ku.dk> > Date: 01 Sep 2000 09:54:59 +0200 > > Prof Brian D Ripley <ripley at stats.ox.ac.uk> writes: Important omission: specification from Murray Jorgensen The test that I was thinking of basically does an anova on a modified response variable that is the absolute value of the difference between an observation

Hello all, I am low down on the learning curve of R but so far I have had little trouble using most of the packages. However, recently I have run into a wall when it comes to bootstrapping a Levene's test (from the car package) and thought you might be able to help. I have not been able to find R examples for the "boot" package where the test statistic specifically uses a

Dear All I am trying to use Levene's test (of package car), but I do not understand quite well how to use it. '?levene.test' does not unfortunately provide any example. My data are in a data frame and correspond to 4 factors plus response. Could someone please give me an example about how to use the command levene.test(y, group) ? Thanks in advance, Paul

Hi All, is there Levene' test in R ? If not ,Could you give me some advice about Levene test with R? Thanks a lot! I am waiting for yours.

Dear all a couple of months ago i've found threads regard test that verify AnOVa assumption on homogeneity of variances. Prof. Ripley advice LDA / QDA procedures, many books (and many proprietary programs) advice Hartley's F_max, Cochran's minimum/maximum variance ratio (only balanced experiments), K^2 Bartlett's test, Levene's test. Morton B. Brown and Alan B. Forsythe in a

Hello, I notice that when I do Levene's test to test equality of variances across levels of a factor, I get different answers in R and SPSS 16. e.g.: For the chickwts data, in R, levene.test(weight, feed) gives F=0.7493, p=0.5896. SPSS 16 gives F=0.987, p=0.432 Why this difference? Which one should I believe? (I would like to believe R :) Ravi -- View this message in context:

Thanks. Eric Leifer, Ph.D. Office of Biostatistics Research National Heart, Lung, and Blood Institute 6701 Rockledge Drive, MSC 8217 Bethesda, MD 20892-8217 Email: LeiferE@NHLBI.NIH.GOV Phone: 301-435-0436 FAX: 301-480-1862

Hallo! I just started working with R to do the statistical analyses for my diploma thesis. I got two sets of data. Both contain repeated measures. One has only one within-subject factor with four levels. The other has one within-subject factor with two levels and one between-factor with two levels. I want to compute a Mauchly test for both sets and a Levene test for the second set. I

Dear all I would like to show my audience that some variables are homogenous inside groups but different outside. I can use by with summary for all variables by(iris[,1:4], iris$Species, summary) what can be quite messy in case of more than few variables and about 8 groups or densityplot for one variable densityplot(~Petal.Length | Species, iris) I have two questions: 1. Is there

Hello, I am searching for the classic Levene test of variances in R but I don't find the function. Is there any command or do I have to programm the test by myself? Does anybody know? Help would be fine because R is very new to me. S.Zank [[alternative HTML version deleted]]

Hi, What package(s) must I install so that I can apply the Levene' test in my data? I tried 'lawstat' but dependency ?VGAM? is not available for this package. I am using Rstudio Version 1.1.383 Tks [[alternative HTML version deleted]]

I am testing the homogeneity of variances via bartlett.test and fligner.test. Using the following example, how should I interpret the p-value in order to accept or reject the null hypothesis ? set.seed(5) x <- rnorm(20) bartlett.test(x, rep(1:5, each=4)) Bartlett test of homogeneity of variances data: x and rep(1:5, each = 4) Bartlett's K-squared = 1.7709, df = 4, p-value =