similar to: Levene's test for homogeneity of variances (befor using ANOVA)

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

Has the Levene test of homogeneity of variance been implemented in any library in R? Thanks, Maurice Haynes National Institute of Child Health and Human Development Child and Family Research Section 6705 Rockledge Drive Bethesda, MD 20892 Voice: 301-496-8180 Fax: 301-496-2766 E-Mail: mh192j at nih.gov

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

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> 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

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

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:

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Dear all, I have finally managed to get a "Analysis of Variance Table": Response: LogHand Df Sum Sq Mean Sq F value Pr(>F) Prey 3 5.3125 1.7708 20.672 2.066e-11 *** Residuals 164 14.0488 0.0857 I want to compare the means of the four different groups, and see if there is a difference. I searched for help,

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.

hello, I try to run simulation of levene's test to find the p-value but the error of replacement has length zero occur, could anyone help me to fix this problem? asim <- 1000 pv<-rep(NA,asim) for(i in 1:asim) {print(i) set.seed(i) g1 <- rnorm(20,0,2) g2 <- rnorm(20,0,2) g3 <- rnorm(20,0,2) x <- c(g1,g2,g3) group<-as.factor(c(rep(1,20),rep(2,20),rep(3,20))) library(Rcmdr)

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 R community, I am looking for a robust test for equality of variances that can take observation weights. I realize I can do the F-test with weighted variances, but I've read that this test is not very robust. So I thought about maybe adding a "weights" argument to John Fox's code for the Levene Test (in the "car" library, "levene.test"), substituting

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]]

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 =

Hello, for my meta-analysis I try to test if two varainces are equal without using the raw scores. I have is the SD's, N's and the Means. I want to test the variances from dependent and independend samples. I assume I can use the var.test procedure for the independent samples, but what about the dependent samples ? Has anyone an idea how to realise this with R ? Thanks in advance

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 r-helpers, Prior to analysis of variance, I ran the Boxcox function (MASS library) to find the best power transformation of my data. However, reading the Boxcox help file, I cannot figure out if this function (through its associated log-likelihood function) corrects for * normality only * or if it also induces * homogeneity of variances *. I found in Biometry (Sokal and Rohlf, p. 419)