similar to: bootstrapping Levene's test

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! 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

> 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, 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:

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

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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)

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 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

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.

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 sent already before a message concerning Levene's test in nested model, but I didn't get any answer. Optimistically I hope to get an answer this time. I also point a new question related to the whole model, because I haven't find any sure answer if I am analysing it in a suitable way or not. I really have tried to do my homework. I have response variable (y) and four

Dear All, I am willing to test homogenity of variances using Levene's test. I know how to do it with simple model, but I am lost when dealing with a little bit more complex model. I have response variable (y) and four factors (a, b, c, d). One of these four factors (d) is nested within another factor (c). In addition, I would like to take into account only 2nd degree interactions in my

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]]

Could someone help me on how to correctly try to correct this error message arning : BCa Intervals used Extreme Quantiles Some BCa intervals may be unstable Warning message: Extreme Order Statistics used as Endpoints in: norm.inter(t, adj.alpha) Regards IF [[alternative HTML version deleted]]

HI everyone, I try to get some bootstrap CIs for coefficients obtained by quantile regression. I have influencial values and thus switched to quantreg.. The data is clustered and within clusters the variance of my DV = 0.. Is this sensible for the below data? And what about the warnings? Thanks in advance for any guidance, Kay > dput(d) structure(list(Porenfläche = c(4990L, 7002L, 7558L,

We have started to use Samba v 3 under SuSe 10. Although I works fine, I'm having some problems with setting up users. I have downloaded and tried Webmin which works well. Does anyone know of a utility that performs the simple functions that a small network administrator wants - Add/remove user (to both Linux and Samba) Add/remove user to Samba (assuming existing Linux account)

Hello, I have a question regarding bootstrap confidence intervals. Suppose we have a data set consisting of single measurements, and that the measurements are independent but the distribution is unknown. If we want a confidence interval for the population mean, when should a bootstrap confidence interval be preferred over the elementary t interval? I was hoping the answer would be

I like to bootstrap regression models, saving the entire set of bootstrapped regression coefficients for later use so that I can get confidence limits for a whole set of contrasts derived from the coefficients. I'm finding that ordinary bootstrap percentile confidence limits can provide poor coverage for odds ratios for binary logistic models with small N. So I'm exploring BCa confidence