[R] Why two chisq.test p values differ when the contingency

[(Ted Harding)]

On 15-Jul-03 Tao Shi wrote:
>>x
>      [,1] [,2]
> [1,]  149  151
> [2,]    1    8
>>t(x)
>      [,1] [,2]
> [1,]  149    1
> [2,]  151    8
>>chisq.test(x, simulate.p.value=T, B=100000)
>         Pearson's Chi-squared test with simulated p-value (based on
> 1e+05 replicates)
> data:  x
> X-squared = 5.2001, df = NA, p-value = 0.03774
> 
>>chisq.test(t(x), simulate.p.value=T, B=100000)
>         Pearson's Chi-squared test with simulated p-value (based on
> 1e+05 replicates)
> data:  t(x)
> X-squared = 5.2001, df = NA, p-value = 0.01642

Possibly you may just have been unlucky, though the 0.03774 seems large:

c2x<-chisq.test(x, simulate.p.value=T, B=100000)$p.value
for(i in (1:9)){c2x<-c(c2x,chisq.test(x, simulate.p.value=T,
                       B=100000)$p.value)}
c2tx<-chisq.test(tx, simulate.p.value=T, B=100000)$p.value
for(i in (1:9)){c2tx<-c(c2tx,chisq.test(tx, simulate.p.value=T,
                        B=100000)$p.value)}
cbind(c2x,c2tx)
          c2x    c2tx
 [1,] 0.01627 0.01720
 [2,] 0.01672 0.01690
 [3,] 0.01662 0.01669
 [4,] 0.01733 0.01656
 [5,] 0.01679 0.01777
 [6,] 0.01715 0.01769
 [7,] 0.01765 0.01769
 [8,] 0.01703 0.01740
 [9,] 0.01704 0.01708
[10,] 0.01669 0.01655

sd(c2x)
 [1] 0.0003946715
sd(c2tx)
 [1] 0.0004737099

Ted.


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E-Mail: (Ted Harding) <Ted.Harding at nessie.mcc.ac.uk>
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Date: 15-Jul-03                                       Time: 21:00:04
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