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 read the
Tukey test is valid only when the model is balanced and when the assumption of
homogeneity of variances is not rejected, am i wrong? Can anyone tell me what
would be the correct test in this case?
Doing a non-parametric Kruskal - wallis test would give me a different result.
But what would be the correct multiple comparison test in this case?
Thank you and have a great day ahead!
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Subject: Re: question for aov and kruskal Newsgroups: R-help:gmane.comp.lang.r.general To: eugen pircalabelu <eugen_pircalabelu at yahoo.com> On 10 Mar 2008, you wrote in gmane.comp.lang.r.general:> 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 read the Tukey test is valid > only when the model is balanced and when the assumption of > homogeneity of variances is not rejected, am i wrong? Can anyone > tell me what would be the correct test in this case? > > Doing a non-parametric Kruskal - wallis test would give me a > different result. But what would be the correct multiple comparison > test in this case? >If you install the coin package and look at ?oneway_test help panel, you will see an implementation of what the authors (citing Hollander and Wolfe) are calling the Nemenyi-Damico-Wolfe-Dunn test. From the example it appears that you also need the multcomp package to run the test. -- David Winsemius
> 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 read the Tukey test is valid only when the model > is balanced and when the assumption of homogeneity of variances is not > rejected, am i wrong? Can anyone tell me what would be the correct test in > this case? > > Doing a non-parametric Kruskal - wallis test would give me a different > result. But what would be the correct multiple comparison test in this > case? >You shouldn't have needed aov to tell you that the data (not the model) are unbalanced. I could see that without running the code! Seriously, you might need to think more about the type of model you're using, and what you want to know, and then consider how to estimate the effect sizes of interest. ----- David Hewitt Virginia Institute of Marine Science http://www.vims.edu/fish/students/dhewitt/ -- View this message in context: http://www.nabble.com/question-for-aov-and-kruskal-tp15955385p15976643.html Sent from the R help mailing list archive at Nabble.com.