Cochrans Q Test

[...]

cochranq.test(K)

       Cochran's Q Test for Dependent Samples

data:  K
Cochran's Q = 23.9298, df = 11, p-value = 0.01303

Cochran's Q fits into the `coin' framework and thus:

 K <- as.table(matrix(c(1,1,0,0, 1,1,0,1, 1,1,1,1, 1,1,1,1, 1,0,0,0,

+  1,1,1,1, 1,1,1,1, 0,0,0,0, 0,1,0,1, 1,1,1,1, 0,1,0,1, 0,1,0,1),ncol=12,
+  dimnames = list("Seating type" = c("I","II","III","IV"),"Test
+  subject"=c("A","B","C","D","E","F","G","H","I","J","K","L"))))

df <- data.frame(success = as.vector(K),

+                  test = factor(rep(colnames(K), rep(4, 12))),
+                  subject = factor(rep(rownames(K), 12)))

library("coin")
symmetry_test(success ~ test | subject, data = df, teststat = "quad")

Asymptotic General Independence Test

data:  success by
          groups A, B, C, D, E, F, G, H, I, J, K, L
          stratified by subject
chi-squared = 23.9298, df = 11, p-value = 0.01303

can be used to compute the test without additional coding and

symmetry_test(success ~ test | subject, data = df, teststat = "quad",

distribution = approximate(10000))

         Approximative General Independence Test

data:  success by
          groups A, B, C, D, E, F, G, H, I, J, K, L
          stratified by subject
chi-squared = 23.9298, p-value = 0.006

approximates the p value by Monte-Carlo procedures.

Best wishes,

Torsten

BTW, a quick Google search shows that the pcochran() function is in the
'outliers' package on CRAN, which also has a cochran.test() function

HTH,

Marc Schwartz

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