From: Weiwei Shi <helprhelp_at_gmail.com>

Date: Sat 09 Jul 2005 - 03:05:42 EST

Date: Sat 09 Jul 2005 - 03:05:42 EST

index word.comb id in.class0 in.class1 p.value odds.ratio 1 1 TOTAL|LAID 54|241 2 4 0.0004997501 0.00736433 2 2 THEFT|RECOV 52|53 40751 146 0.0004997501 4.17127643 3 3 BOLL|ACCID 10|21 36825 1202 0.0004997501 0.44178546 4 4 LAB|VANDAL 8|55 24192 429 0.0004997501 0.82876099 5 5 VANDAL|CAUS 55|59 801 64 0.0004997501 0.18405918 6 6 AI|TOTAL 9|54 1949 45 0.0034982509 0.63766766 7 7 AI|RECOV 9|53 2385 61 0.0004997501 0.57547012 8 8 THEFT|TOTAL 52|54 33651 110 0.0004997501 4.56174408

the target is to look for best subset of word.comb to differentiate
between class0 and class1. p.value is obtained via
p.chisq.sim[i] <- as.double(chisq.test(tab, sim=TRUE, B=myB)$p.value)
and B=20000 (I increased B and it won't help. the margin here is
class0=2162792

class1=31859

)

So, in conclusion, which one I should use first, odds.ratio or p.value to find the best subset.

On 6/24/05, Gabor Grothendieck <ggrothendieck@gmail.com> wrote:

> On 6/24/05, Kjetil Brinchmann Halvorsen <kjetil@acelerate.com> wrote:

*> > Weiwei Shi wrote:
**> >
**> > >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 tables
**> > with 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
**> > >
**>
**>
**> Here are a few more ways of doing this using chisq.test,
**> glm and assocplot:
**>
**> > ## chisq.test ###
**>
**> > tab1.chisq <- chisq.test(tab1)
**>
**> > # decomposition of chisq
**> > resid(tab1.chisq)^2
**> [,1] [,2]
**> [1,] 0.1451026 0.0007570624
**> [2,] 9.8504915 0.0513942218
**>
**> > # same
**> > with(tab1.chisq, (observed - expected)^2/expected)
**> [,1] [,2]
**> [1,] 0.1451026 0.0007570624
**> [2,] 9.8504915 0.0513942218
**>
**>
**> > # Pearson residuals
**> > resid(tab1.chisq)
**> [,1] [,2]
**> [1,] 0.3809234 -0.02751477
**> [2,] -3.1385493 0.22670294
**>
**> > # same
**> > with(tab1.chisq, (observed - expected)/sqrt(expected))
**> [,1] [,2]
**> [1,] 0.3809234 -0.02751477
**> [2,] -3.1385493 0.22670294
**>
**>
**> > ### glm ###
**> > # Pearson residuals via glm
**>
**> > tab1.df <- data.frame(count = c(tab1), A = gl(2,2), B = gl(2,1,4))
**> > tab1.glm <- glm(count ~ ., tab1.df, family = poisson())
**> > resid(tab1.glm, type = "pearson")
**> 1 2 3 4
**> 0.38092339 -3.13854927 -0.02751477 0.22670294
**> > plot(tab1.glm)
**>
**> > ### assocplot ###
**> > # displaying Pearson residuals via an assocplot
**> > assocplot(t(tab1))
**>
*

-- Weiwei Shi, Ph.D "Did you always know?" "No, I did not. But I believed..." ---Matrix III ______________________________________________ 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 Jul 09 03:11:13 2005

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