FAQ
Hi Tao:

The P-values for 2x2 table are generated based on a random (discrete
uniform distribution) sampling of all possible 2x2 tables, conditioning
on the observed margin totals. If one of the cells is extremely small,
as in your case, you get a big difference in P-values. Suppose, you
changed the cell with value 1 to, say, 5 or 6, then the two P-values
are nearly the same. However, I don't understand why they should be so
different, since the set of all possible 2x2 tables will be the same in
both cases. I would be interested in knowing how this happens.

Ravi.

----- Original Message -----
From: "Shi, Tao" <shidaxia@yahoo.com>
Date: Tuesday, July 15, 2003 4:37 pm
Subject: RE: [R] Why two chisq.test p values differ when the contingency
Hi, Ted and Dennis:

Thanks for your speedy replies! I don't think this happens just
randomly, rather, I'm thinking it may be due to the way chisq.test
function handles simulation. Here shows why: (Ted, I think there
is an error in your code, "tx" should be t(x) )
x
[,1] [,2]
[1,] 149 151
[2,] 1 8
c2x<-chisq.test(x, simulate.p.value=T, B0000)\$p.value
for(i in (1:20)){c2x<-c(c2x,chisq.test(x, simulate.p.value=T,
+ B0000)\$p.value)}
c2tx<-chisq.test(t(x), simulate.p.value=T, B0000)\$p.value
for(i in (1:20)){c2tx<-c(c2tx,chisq.test(t(x), simulate.p.value=T,
+ B0000)\$p.value)}
cbind(c2x,c2tx)
c2x c2tx
[1,] 0.03727 0.01629
[2,] 0.03682 0.01662
[3,] 0.03671 0.01665
[4,] 0.03788 0.01745
[5,] 0.03706 0.01646
[6,] 0.03715 0.01728
[7,] 0.03664 0.01683
[8,] 0.03681 0.01720
[9,] 0.03742 0.01758
[10,] 0.03712 0.01685
[11,] 0.03739 0.01615
[12,] 0.03811 0.01653
[13,] 0.03711 0.01673
[14,] 0.03639 0.01678
[15,] 0.03714 0.01719
[16,] 0.03774 0.01780
[17,] 0.03574 0.01707
[18,] 0.03661 0.01705
[19,] 0.03751 0.01711
[20,] 0.03683 0.01718
[21,] 0.03678 0.01653

...Tao

===========================================================> Ted.Harding at nessie.mcc.ac.uk wrote:
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0000)
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0000)
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0000)\$p.value
for(i in (1:9)){c2x<-c(c2x,chisq.test(x, simulate.p.value=T,
B0000)\$p.value)}
c2tx<-chisq.test(tx, simulate.p.value=T, B0000)\$p.value
for(i in (1:9)){c2tx<-c(c2tx,chisq.test(tx, simulate.p.value=T,
B0000)\$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.

-------------------------------------------------------------------
-
E-Mail: (Ted Harding)
Fax-to-email: +44 (0)870 167 1972
Date: 15-Jul-03 Time: 21:00:04
------------------------------ XFMail -----------------------------
-

---------------------------------

[[alternative HTML version deleted]]

______________________________________________
R-help at stat.math.ethz.ch mailing list
https://www.stat.math.ethz.ch/mailman/listinfo/r-help

## Related Discussions

 view thread | post posts ‹ prev | 4 of 8 | next ›
Discussion Overview
 group r-help categories r posted Jul 15, '03 at 7:18p active Jul 16, '03 at 10:33a posts 8 users 5 website r-project.org irc #r

### 5 users in discussion

Content

People

Support

Translate

site design / logo © 2018 Grokbase