>Date: Wed, 16 Jul 2003 01:27:25 +0200 (MET DST)
>From: [EMAIL PROTECTED]
>> x
> [,1] [,2]
>[1,] 149 151
>[2,] 1 8
>> c2x<-chisq.test(x, simulate.p.value=T, B=100000)$p.value
>> for(i in (1:20)){c2x<-c(c2x,chisq.test(x,
>simulate.p.value=T,B=100000)$p.value)}
>> c2tx<-chisq.test(t(x), simulate.p.value=T, B=100000)$p.value
>> for(i in (1:20)){c2tx<-c(c2tx,chisq.test(t(x), simulate.p.value=T,
>+ B=100000)$p.value)}
>> cbind(c2x,c2tx)
> c2x c2tx
> [1,] 0.03711 0.01683
> [2,] 0.03717 0.01713
The problem is in ctest/R/chisq.test.R, where the p-value is
calculated as
STATISTIC <- sum((x - E) ^ 2 / E)
PARAMETER <- NA
PVAL <- sum(tmp$results >= STATISTIC) / B
Here tmp$results is a collection of simulated chisquare values, but
because of different rounding, the statistics corresponding to tables
equal to the observed table are slightly smaller than the value
calculated in STATISTIC, and effectively the p-value is calcuated as
PVAL <- sum(tmp$results > STATISTIC) / B
instead.
What's the appropriate fix here?
PVAL <- sum(tmp$results > STATISTIC - .Machine$double.eps^0.5) / B
works on this example, but is there something better?
Duncan Murdoch
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