# Re: [R] Cochrans Q Test

From: Marc Schwartz <MSchwartz_at_mn.rr.com>
Date: Mon 18 Sep 2006 - 13:04:05 GMT

On Mon, 2006-09-18 at 14:01 +1000, peter.gremse@numbers.net.au wrote:
> Hi!
>
> I would like to conduct a Cochran`s Q Test in R, but have not found any
> suitable function.
>
> My first idea was: J <- as.table(matrix(c(6,16,2,4),ncol=2, dimnames =
> list("C" = c("Favorable","Unfavorable"),"Drug A Favorable"=c("B
> Favorable","B Unfavorable"))))
> L <- as.table(matrix(c(2,4,6,6),ncol=2, dimnames = list("C" =
> c("Favorable","Unfavorable"),"Drug A Unfavorable"=c("B Favorable","B
> Unfavorable"))))
> mantelhaen.test(J,L, alternative="t")
>
> But this is obviously the wrong function.

The CMH test does not consider the dependent nature of the measurements, so it is indeed the wrong test, if you have dependent measures as your data 'K' below would suggest.

Cochran's Q is a generalization of the McNemar paired chi square.

> Then I googled and found (different data):
>
> 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"))))
> K
> pcochran(K,4,12)
>
> But R said that this function does not exist.
> Can anyone help?

Here is a version of the Cochran's Q that I wrote and posted to sci.stat.consult back in June in response to a thread there. This is based upon Sheskin's Handbook of Parametric and Nonparametric Statistical Procedures (2004) page 867. You might want to secure a copy of the book and review the comments regarding the assumptions underlying this test and the considerations for it use.

cochranq.test <- function(mat)
{
k <- ncol(mat)

```  C <- sum(colSums(mat) ^ 2)
R <- sum(rowSums(mat) ^ 2)
T <- sum(rowSums(mat))

```

num <- (k - 1) * ((k * C) - (T ^ 2))
den <- (k * T) - R

Q <- num / den

df <- k - 1
names(df) <- "df"
names(Q) <- "Cochran's Q"

p.val <- pchisq(Q, df, lower = FALSE)

QVAL <- list(statistic = Q, parameter = df, p.value = p.val,

```               method = "Cochran's Q Test for Dependent Samples",
data.name = deparse(substitute(mat)))
```
class(QVAL) <- "htest"
return(QVAL)
}

> K

Test
subject
Seating type A B C D E F G H I J K L

```         I   1 1 1 1 1 1 1 0 0 1 0 0
II  1 1 1 1 0 1 1 0 1 1 1 1
III 0 0 1 1 0 1 1 0 0 1 0 0
IV  0 1 1 1 0 1 1 0 1 1 1 1

```

Cochran's Q Test for Dependent Samples

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

HTH, Marc Schwartz

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