search for: fligner

Full_Name: Karel Zvara Version: 1.8.1 OS: MS Winows 2000 Submission from: (NULL) (195.113.30.163) The test statistics of the fligner.test (ctest package) depends on the order of cases: > fligner.test(count~spray,data=InsectSprays) Fligner-Killeen test for homogeneity of variances data: count by spray Fligner-Killeen:med chi-squared = 14.4828, df = 5, p-value = 0.01282 > fligner.test(count~spray,data=InsectSpr...

I've made fligner test with the same data, changing the orders of the variables, and this what i get > fligner.test(rojos~edadysexo*zona*ano*estacion) Fligner-Killeen test of homogeneity of variances data: rojos by edadysexo by zona by ano by estacion Fligner-Killeen:med chi-squared = 15.7651, df = 2...

...A sample of my data is as follows: dT Topology Drift lambda 258.789 Tree b1 .43 244.195 Tree b1 .43 115.961 Tree b2 .3 115.183 Tree b2 .3 I would like to separate dT in the 32 samples (4x4x2), and test if the variance of each sample is equal to the other 31 samples. I tried using fligner.test and bartlett.test, but either test seems to only work for one factor: > fligner.test( dT ~ Topology + Drift + lambda) Fligner-Killeen test of homogeneity of variances data: dT by Topology by Drift by lambda Fligner-Killeen:med chi-squared = 15.4343, df = 2, p-value = 0.0004451...

>Ben Bolker <bolker <at> ufl.edu> writes: > >I would try > >fligner.test(dT ~ Topology:Drift:lambda) > >in response to: > >Javier Acuna <javier.acuna.o <at> gmail.com> writes: > > Hi, I'm a new user of R. My background is Electrical Engineering, so > please bear with me if this is a silly question. > > I'm trying to a...

In specific cases fligner.test() can produce a small p-value even when both groups have constant variance. Here is an illustration: fligner.test(c(1,1,2,2), c("a","a","b","b")) # p-value = NA But: fligner.test(c(1,1,1,2,2,2), c("a","a","a"...

Can anybody tell me if there is an R implementation of the Fligner-Policello robust rank test? Thanks, Elisabetta

Hello, I investigate survival until the following year (0,1) and I wish to test if the variance in survival for two or more groups are significantly different from each other. I read that the Fligner-Killeen test is a non-parametric test which is very robust against departures from normality but is it correct (valuable technique for publication) to use it on binary data? In other words, can I use fligner.test(survival~categorical_predictor,data=mydata) when survival is binary (0,1)? Bes...

...<emeline.mourocq at uzh.ch> wrote: > > Hello, > > > > I investigate survival until the following year (0,1) and I wish to test if > the variance in survival for two or more groups are significantly different > from each other. > > > > I read that the Fligner-Killeen test is a non-parametric test which is very > robust against departures from normality but is it correct (valuable > technique for publication) to use it on binary data? > > > > In other words, can I use > fligner.test(survival~categorical_predictor,data=mydata) when...

In specific cases fligner.test() can produce a small p-value even when both groups have constant variance. Here is an illustration: fligner.test(c(1,1,2,2), c("a","a","b","b")) # p-value = NA But: fligner.test(c(1,1,1,2,2,2), c("a","a","a"...

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 =...

> 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:

Hi R users! I have the following problem: how appropriate is my aov model under the violation of anova assumptions? Example: a<-c(1,1,1,1,1,1,1,1,1,1,2,2,2,3,3,3,3,3,3,3) b<-c(101,1010,200,300,400, 202, 121, 234, 55,555,66,76,88,34,239, 30, 40, 50,50,60) z<-data.frame(a, b) fligner.test(z$b, factor(z$a)) aov(z$b~factor(z$a))->ll TukeyHSD(ll) Now from the aov i found that my model is unbalanced, and from the flinger test i found out that the assumption of homogeneity of variances is rejected. Could my Tukey comparison be a valid one under these violations? From what i r...

...n R and statistics. I?ve got a model of this kind: binary response variable: prevalence of infection (0/1) 3 categorical independent variables: sex, month and name of the area I was trying with a full model like this, before the simplification model<-aov(prevalencia~sex*month*area) but the Fligner test told that i haven?t got homoscedascity, so I suppose I should trying with glm, with a model model2<-glm(prevalencia~edad*sexo*mes*zona,binomial) is that correct? where I must put the link (logit) ? Thnks very much -- View this message in context: http://r.789695.n4.nabble.com/Don-t-know...

Hello Using R 2.2.1 on a Windows machine. Has anyone programmed the Welch test for equality of variances? I tried RSiteSearch, but this gave references to t test and oneway.test, which are not quite what I need.....I need the Welch test itself, for use in a meta-analysis (to determine if variances are equal). TIA Peter Peter L. Flom, PhD Assistant Director, Statistics and Data Analysis

...Dist.,family="quasipoisson",data=d) scatter.smooth(d$Dist.,s,ylab="D. aethiops Length (mm)",xlab="Distance from Shoreline (m)",col="red") summary(a1) a2<-aov(s~d$Dist.,data=d) summary(a2) shapiro.test(a2$residuals) qqnorm(a2$residuals) qqline(a2$residuals) fligner.test(s~d$Dist.,data=d) kruskal.test(s~d$Dist.,data=d) df<-df.residual(a1) t<-qt(.975,df) k<-data.frame(s=seq(10.5,max(s,na.rm=T),1)) p<-predict(a1,k,se=T) est<-exp(p$fit) low<-exp(p$fit-t*p$se.fit) hight<-exp(p$fit-t*p$se.fit) plot(d$Dist.,s,ylim=c(min(s),max(s)),xlab="Dis...

...r independence, with multiple factors To: bolker@ufl.edu, r-help@r-project.org Message-ID: <e10c29610809211401i6e3d7792p22b72172993aae9a@mail.gmail.com> Content-Type: text/plain; charset=ISO-8859-1 >Ben Bolker <bolker <at> ufl.edu> writes: > >I would try > >fligner.test(dT ~ Topology:Drift:lambda) > >in response to: > >Javier Acuna <javier.acuna.o <at> gmail.com> writes: > > Hi, I'm a new user of R. My background is Electrical Engineering, so > please bear with me if this is a silly question. > > I'm trying to a...

...ummaryfn <- function(x)  data.frame(n=sum(complete.cases(x)),mean=mean(x),std.dev=sd(x), t.value=mean(x)/(sd(x)/sqrt(sum(complete.cases(x)))), p.value=2*pt(-abs(mean(x)/(sd(x)/sqrt(sum(complete.cases(x))))),df=sum(complete.cases(x))-1))   cfev <- na.omit(fev) by(cfev[3:5],cfev[2],summaryfn)   fligner.test(chg ~ trtgrp, data=fev) t.test(chg ~ trtgrp, data=fev, var.equal=TRUE)   #summaryfn <- function(x) #   data.frame(n=sum(complete.cases(x)),mean=mean(x),std.dev=sd(x), #    t.value=t.test(x)$statistic, #    p.value=t.test(x)$p.value) #cfev <- na.omit(fev) #by(cfev[3:5],cfev[2],summaryfn)...

Hi I am trying to conduct a Bartlett test between two groups Samp 1 and Samp 2, both of which are vectors of equal length. I cant find any information on how to do this. Does the data need to be in a structured list. Thanks in advance

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.