[R] Cochrans Q Test
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. 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? __ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Re: [R] Cochrans Q Test
On Mon, 2006-09-18 at 14:01 +1000, [EMAIL PROTECTED] 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)
}
Using your matrix 'K':
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
cochranq.test(K)
Cochran's Q Test for Dependent Samples
data: K
Cochran's Q = 23.9298, df = 11, p-value = 0.01303
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
__
R-help@stat.math.ethz.ch mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
Re: [R] 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(1)) 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 __ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. __ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
