Question about cochran test in R

Thanks a lot for your replies Henry!

Your answer was specially a bless! Many thanks this was an issue which
was breaking my head.

I have another couple of questions, may be you could help me. For post
hoc comparison I was planning to run a McNemar test with a bonferroni
correction, but wanted to be sure my approach is correct.

Sorry if I annoy you with this remaining question.

Thanks in advance!

2015-05-07 8:03 GMT-03:00 Henric Winell <nilsson.henric at gmail.com
<mailto:nilsson.henric at gmail.com>>:

    On 2015-05-07 09:15, Jim Lemon wrote:

        Hi Luis,
        Try this page:

        http://www.r-bloggers.com/cochran-q-test-for-k-related-samples-in-r/

        Jim


    Cochran's Q test is a marginal homogeneity test, and such tests can
    be performed by the 'mh_test' function in the 'coin' package.  The
    following replicates the result in the blog post

     > library("coin")
     >
     > dta <- data.frame(

    +     method    = factor(rep(LETTERS[1:4], 6)),
    +     repellent = factor(c(1, 1, 0, 0,
    +                          1, 1, 0, 1,
    +                          1, 0, 0, 0,
    +                          1, 1, 1, 0,
    +                          1, 1, 0, 1,
    +                          1, 1, 0, 1)),
    +     fabric    = gl(6, 4, labels = as.roman(1:6))
    + )

     >
     > mh_test(repellent ~ method | fabric, data = dta)

             Asymptotic Marginal-Homogeneity Test

    data:  repellent by
              method (A, B, C, D)
              stratified by fabric
    chi-squared = 9.3158, df = 3, p-value = 0.02537


    and uses the asymptotic approximation to compute the p-value.  The
    'coin' package also allows you to approximate the exact null
    distribution using Monte Carlo methods:

     > set.seed(123)
     > mh_test(repellent ~ method | fabric, data = dta,

    +         distribution = approximate(B = 10000L))

             Approximative Marginal-Homogeneity Test

    data:  repellent by
              method (A, B, C, D)
              stratified by fabric
    chi-squared = 9.3158, p-value = 0.0202


    For future reference, 'mh_test' is fairly general and handles both
    matched pairs or matched sets.  So, the well-known tests due
    McNemar, Cochran, Stuart(-Maxwell) and Madansky are just special cases.

    For more general symmetry test problems, the 'coin' package offers
    the 'symmetry_test' function and this can be used to perform, e.g.,
    multivariate marginal homogeneity tests like the multivariate
    McNemar test (Klingenberg and Agresti, 2006) or the multivariate
    Friedman test (Gerig, 1969).


    Henric






        On Thu, May 7, 2015 at 4:59 PM, Luis Fernando Garc?a
        <luysgarcia at gmail.com <mailto:luysgarcia at gmail.com>> wrote:

            Dear R Experts,

            May be this is a basic question for you, but it is something
            I need really
            urgently. I need to perform a Chi Square analysis for more
            than two groups
            of paired observations. It seems to be ok For Cochran test.
            Unfortunately I
            have not found info about  this test in R, except for
            dealing with outliers
            which is not my aim. I am looking for something like this
            https://www.medcalc.org/manual/cochranq.php

            I found a video to perform this analysis in R, but was not
            specially
            useful. Does some of you know have some info about how to
            make this
            analysis in R?

            Thanks in advance!

                      [[alternative HTML version deleted]]