From: Kjetil Brinchmann Halvorsen <kjetil_at_acelerate.com>

Date: Sat 25 Jun 2005 - 05:27:28 EST

Help for 'odds.ratio' is shown in the browser > library(help=epitools) # on CRAN

> tab1

> library(vcd) # on CRAN

[1,] 0.1451026 0.0007570624

[2,] 9.8504915 0.0513942218

>

Date: Sat 25 Jun 2005 - 05:27:28 EST

*>Hi,
*

>I asked this question before, which was hidden in a bunch of

*>questions. I repharse it here and hope I can get some help this time:
**>
**>I have 2 contingency tables which have the same group variable Y. I
**>want to compare the strength of association between X1/Y and X2/Y. I
**>am not sure if comparing p-values IS the way even though the
**>probability of seeing such "weird" observation under H0 defines
**>p-value and it might relate to the strength of association somehow.
**>But I read the following statement from Alan Agresti's "An
**>Introduction to Categorical Data Analysis" :
**>"Chi-squared tests simply indicate the degree of EVIDENCE for an
**>association....It is sensible to decompose chi-squared into
**>components, study residuals, and estimate parameters such as odds
**>ratios that describe the STRENGTH OF ASSOCIATION".
**>
**>
**>
*

Here are some things you can do:

> tab1<-array(c(11266, 125, 2151526, 31734), dim=c(2,2))

> tab2<-array(c(43571, 52, 2119221, 31807), dim=c(2,2)) > library(epitools) # on CRAN > ?odds.ratio

Help for 'odds.ratio' is shown in the browser > library(help=epitools) # on CRAN

> tab1

[,1] [,2]

[1,] 11266 2151526

[2,] 125 31734

> odds.ratio(11266, 125, 2151526, 31734)
Error in fisher.test(tab) : FEXACT error 40.

Out of workspace. # so this are evidently for tableswith smaller counts

> library(vcd) # on CRAN

> ?oddsratio

Help for 'oddsratio' is shown in the browser
> oddsratio( tab1) # really is logodds ratio
[1] 0.2807548

> plot(oddsratio( tab1) ) > library(help=vcd) # on CRAN Read this for many nice functions. > fourfoldplot(tab1) > mosaicplot(tab1) # not really usefull for this table

Also has a look at function Crosstable in package gmodels.

To decompose the chisqure you can program yourselves:

decomp.chi <- function(tab) {

rows <- rowSums(tab) cols <- colSums(tab) N <- sum(rows) E <- rows %o% cols / N contrib <- (tab-E)^2/E contrib } > decomp.chi(tab1) [,1] [,2]

[1,] 0.1451026 0.0007570624

[2,] 9.8504915 0.0513942218

>

So you can easily see what cell contributes most to the overall chisquared.

Kjetil

>Can I do this "decomposition" in R for the following example including

*>2 contingency tables?
**>
**>
**>
**>>tab1<-array(c(11266, 125, 2151526, 31734), dim=c(2,2))
**>>tab1
**>>
**>>
**> [,1] [,2]
**>[1,] 11266 2151526
**>[2,] 125 31734
**>
**>
**>
**>>tab2<-array(c(43571, 52, 2119221, 31807), dim=c(2,2))
**>>tab2
**>>
**>>
**> [,1] [,2]
**>[1,] 43571 2119221
**>[2,] 52 31807
**>
**>
**>BTW, is there some good forum on the theory of statistics? r-help is a
**>good one but I don't want to bother people by asking some questions
**>weakly associated with R here.
**>
**>Thanks,
**>
**>
**>
*

-- Kjetil Halvorsen. Peace is the most effective weapon of mass construction. -- Mahdi Elmandjra -- No virus found in this outgoing message. Checked by AVG Anti-Virus. ______________________________________________ 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.htmlReceived on Sat Jun 25 06:08:41 2005

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