similar to: Homogeneity of variance tests between more than 2 samples (long)

Please consider a data frame A B C D 1 4 5 0 2 3 2 75 3 4 1 84 4 5 1 90 5 3 0 100 Is there a way to plot column B and C as barplot *and* D as line on the same graph?? R-2.0.1 powered by Mandrake 10.1 ------------------------------------------------------------------------------------------------------------------------- Landini dr. Massimiliano Tel. mob. (+39) 347 140 11 94 Tel./Fax. (+39)

Dear all ReadeRs I was finding a quick method to improve test readability adding or constructing (with your help....) one or more function that allow what follow. Please consider Trt<-c(1,1,1,1,2,2,2,2,3,3,3,3,4,4,4,4,5,5,5,5) Block<-c(1,2,3,4,1,2,3,4,1,2,3,4,1,2,3,4,1,2,3,4) AD2DAT<-c(1.44,2.32,1.68,1.28,0.12,0.08,0.24,0.52,0.12,0.08,0.16,0.28,0.16,0.08,0.12,0.12,0.16,0.08,0.32,0.76)

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 =

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)

En innebygd og tegnsett-uspesifisert tekst ble skilt ut... Navn: ikke tilgjengelig Nettadresse: https://stat.ethz.ch/pipermail/r-help/attachments/20080214/5a98c799/attachment.pl

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! 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

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

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

Dear R users, Is it reasonable to transform data (measurements of plant height) to the power of 1/4? I?ve used boxcox(response~A*B) and lambda was close to 0.25. Regards, Christoph

Dear List, I am trying to get R's terminal output to a file and to the terminal at the same time, so that I can walk through some tests and keep a log concurrently. The function 'sink' with the option split=TRUE seems to do just that. It works fine for most output but for objects of class htest, the terminal output is incomplete (the lines are there but empty). Here is an

Hi List. It may be a very simple question, but I couldn’t find an answer on the internet. Which function (maybe in a specific package) would I use to perform a Cochran C Test for Homogeneity of Variances? Stats package have the mantelhaen.test, but I’m “almost sure” that It’s not what I want, it’s probably the Cochran Q Test. So, any ideas? Thank you for the pacience.

Dear all, I'm a beginner of R and I need to carry out some three-way mixed ANOVAs. Following examples at http://personality-project.org/r/r.anova.html, I managed to get the ANOVA part, but I don't know how can I check data normality and homogeneity of variance in R (since they're the required assumptions of ANOVA analysis). Are there any special command/packages? Could anyone give me

Dear all, I have been trying to find a function to calculate the Breslow-Day test for homogeneity of the odds ratio in R. I know the test can be preformed in SAS but i was wondering if anyone could help me to perform this in r. In addition i have the fullrefman file to search for functions in the basic R packages, does anyone have any suggestions of an efficient way of searching for

Hello all, I'm running very simple regression but face a problem of non-homogeneity of variance, but with a decreasing variance with increasing mean...I do not know how to deal with that. this relationship doesn't seem to be strong, but it's my first time to see something like that, and would like to know what to do if one day it becomes stronger. I tested just for fun some

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, We've got a dataset with several variables, one of which we're using to split the data into 3 smaller subsets. (as the variable takes 1 of 3 possible values). There are several more variables too, many of which we're using to fit regression models using lm. So I have 3 models fitted (one for each subset of course), each having slope estimates for the predictor variables.

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

stubdom: add v?errx? and v?warnx? functions Signed-off-by: Samuel Thibault <samuel.thibault@eu.citrix.com> diff -r 14a9a1629590 extras/mini-os/include/posix/err.h --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/extras/mini-os/include/posix/err.h Wed Aug 27 10:29:36 2008 +0100 @@ -0,0 +1,15 @@ +#ifndef _POSIX_ERR_H +#define _POSIX_ERR_H + +#include <stdarg.h> + +void err(int eval,

I've been searching around for a function for computing the Brown-Forsythe F* statistic which is a substitute for the normal ANOVA F statistic for when there are unequal variances, and when there is evidence of non-normality. A couple of other people have asked this question, the responses I found have been: ?oneway.test However, that function appears to use the Welch W statistic which,