The following post would not answer your question at full, but might give
some good code/information:
http://www.r-statistics.com/2010/02/post-hoc-analysis-for-friedmans-test-r-code/




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On Sun, Feb 19, 2012 at 8:25 PM, <saschaview@gmail.com> wrote:

> Some attribute x from 17 individuals was recorded repeatedly on 6 time
> points using a Likert scale with 7 distractors. Which statistical test(s)
> can I apply to check whether the changes along the 6 time points were
> significant?
>
> set.seed( 123 )
> x <- matrix( sample( 1:7, 17*6, repl=T ),
>  nrow = 17, byrow = TRUE,
>  dimnames = list(1:17, paste( 'T', 1:6, sep='' ))
> )
>
> I found the Friedman test and the Quade test for testing the overall
> hypothesis.
>
> friedman.test( x )
> quade.test( x )
>
> However, the R help files, my text books (Bortz, Lienert and Boehnke,
> 2008; Köhler, Schachtel and Voleske, 2007; both German), and the Wikipedia
> texts differ in what they propose as requirements for the tests. R says
> that data need to be unreplicated. I read 'unreplicated' as 'not-repeated',
> but is that right? If so, the example, in contrast, in friedman.test()
> appears to use indeed repeated measures. Yet, Wikipedia says the contrary
> that is to say the test is good especially if data represents repeated
> measures. The text books say either (in the same paragraph, which is very
> confusing). What is right?
>
> In addition, what would be an appropriate test for post-hoc single
> comparisons for the indication which column differs from others
> significantly?
>
> Bortz, Lienert, Boehnke (2008). Verteilungsfreie Methoden in der
> Biostatistik. Berlin: Springer
> Köhler, Schachtel, Voleske (2007). Biostatistik: Eine Einführung für
> Biologen und Agrarwissenschaftler. Berlin: Springer
>
> --
> Sascha Vieweg, saschaview@gmail.com
>
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