Tests for independence where each row of the rx2 table is compared to the exposure reference level and test of independence twosided p values are calculated using midp exact, Fisher's Exact, and the chisquare test.
1 2 3  tab2by2.test(x, y = NULL,
correction = FALSE,
rev = c("neither", "rows", "columns", "both"))

x 
input data can be one of the following: r x 2 table, vector
of numbers from a contigency table (will be transformed into r x 2
table in rowwise order), or single factor or character vector that
will be combined with 
y 
single factor or character vector that will be combined with

correction 
set to TRUE for Yate's continuity correction (default is FALSE) 
rev 
reverse order of "rows", "colums", "both", or "neither" (default) 
Tests for independence where each row of the rx2 table is compared to the exposure reference level and test of independence twosided p values are calculated using midp exact, Fisher's Exact, and the chisquare test.
This function expects the following table struture:
1 2 3 4 5 6  disease=0 disease=1
exposed=0 (ref) n00 n01
exposed=1 n10 n11
exposed=2 n20 n21
exposed=3 n30 n31

The reason for this is because each level of exposure is compared to the reference level.
If you are providing a 2x2 table order does not matter:
If the table you want to provide to this function is not in the
preferred form, just use the rev
option to "reverse" the rows,
columns, or both. If you are providing categorical variables (factors
or character vectors), the first level of the "exposure" variable is
treated as the reference. However, you can set the reference of a
factor using the relevel
function.
Likewise, each row of the rx2 table is compared to the exposure reference level and test of independence twosided p values are calculated using midp exact, Fisher's Exact, Monte Carlo simulation, and the chisquare test.
x 
table that was used in analysis 
p.value 
p value for test of independence 
correction 
logical specifying if continuity correction was used 
Tomas Aragon, aragon@berkeley.edu, http://www.phdata.science
Kenneth J. Rothman and Sander Greenland (1998), Modern Epidemiology, LippincottRaven Publishers
Kenneth J. Rothman (2002), Epidemiology: An Introduction, Oxford University Press
Nicolas P. Jewell (2004), Statistics for Epidemiology, 1st Edition, 2004, Chapman & Hall, pp. 7381
1 2 3 4 5 6 7 8  ##Casecontrol study assessing whether exposure to tap water
##is associated with cryptosporidiosis among AIDS patients
tapw < c("Lowest", "Intermediate", "Highest")
outc < c("Case", "Control")
dat < matrix(c(2, 29, 35, 64, 12, 6),3,2,byrow=TRUE)
dimnames(dat) < list("Tap water exposure" = tapw, "Outcome" = outc)
tab2by2.test(dat, rev="c")

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