Chuck Cleland wrote:
> On 12/12/2008 3:29 AM, robert-mcfad...@o2.pl wrote:
>> Hello,
>> Which package allows to use Cochrana-Armitage trend test? I tried to search
>> for but I found only package coin in which there is no explicit function.
>
> But there is this example in coin:
>
> ### Cochran-Armitage trend test for proportions
> ### Lung tumors in female mice exposed to 1,2-dichloroethane
> ### Encyclopedia of Biostatistics (Armitage & Colton, 1998),
> ### Chapter Trend Test for Counts and Proportions, page 4578, Table 2
> lungtumor <- data.frame(dose = rep(c(0, 1, 2), c(40, 50, 48)),
> tumor = c(rep(c(0, 1), c(38, 2)),
> rep(c(0, 1), c(43, 7)),
> rep(c(0, 1), c(33, 15))))
> table(lungtumor$dose, lungtumor$tumor)
>
> ### Cochran-Armitage test (permutation equivalent to correlation
> ### between dose and tumor), cf. Table 2 for results
> independence_test(tumor ~ dose, data = lungtumor, teststat = "quad")
>
> See the following:
>
> http://finzi.psych.upenn.edu/R/library/coin/html/ContingencyTests.html
Also prop.trend.test().
There seems to be a subtle difference, though:
> independence_test(tumor ~ dose, data = lungtumor, teststat = "quad")
Asymptotic General Independence Test
data: tumor by dose
chi-squared = 10.6381, df = 1, p-value = 0.001108
> tt <- table(lungtumor$dose, lungtumor$tumor)
> prop.trend.test(tt[,2],rowSums(tt))
Chi-squared Test for Trend in Proportions
data: tt[, 2] out of rowSums(tt) ,
using scores: 1 2 3
X-squared = 10.7157, df = 1, p-value = 0.001062
Anyone have a guess at what the difference is? (Just curious.)
-pd
>
> There also is an implementation in the GeneticsBase package
> (Bioconductor).
>
>> Best,
>> RobMac
>>
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>
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