Description Usage Arguments Value Note Author(s) References See Also Examples
Computes for each row of a matrix the value of Pearson's ChiSquare statistic for testing if the corresponding categorical variable is associated with a (categorical) response, or determines for each pair of rows of a matrix the value of Pearson's ChiSquare statistic for testing if the two corresponding variables are independent.
1  rowChisqStats(data, cl, compPval = TRUE, asMatrix = TRUE)

data 
a numeric matrix consisting of the integers between 1 and n.cat,
where n.cat is the maximum number of levels the categorical variables can
take. Each row of 
cl 
a numeric vector of length 
compPval 
should also the pvalue (based on the approximation to a ChiSquaredistribution) be computed? 
asMatrix 
should the pairwise test scores be returned as matrix? Ignored
if 
If compPval = FALSE
, a vector (or matrix if cl
is not specified and
as.matrix = TRUE
) composed of the values of Pearson's ChiSquarestatistic.
Otherwise, a list consisting of
stats 
a vector (or matrix) containing the values of Pearson's ChiSquarestatistic. 
df 
a vector (or matrix) comprising the degrees of freedom of the asymptotic ChiSquaredistribution. 
rawp 
a vector (or matrix) containing the (unadjusted) pvalues. 
Contrary to chisq.test
, currently no continuity correction is done
for 2 x 2 tables.
Holger Schwender, holger.schwender@udo.edu
Schwender, H.\ (2007). A Note on the Simultaneous Computation of Thousands of Pearson's ChiSquareStatistics. Technical Report, SFB 475, Deparment of Statistics, University of Dortmund.
computeContCells
, computeContClass
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52  ## Not run:
# Generate an example data set consisting of 5 rows (variables)
# and 200 columns (observations) by randomly drawing integers
# between 1 and 3.
mat < matrix(sample(3, 1000, TRUE), 5)
rownames(mat) < paste("SNP", 1:5, sep = "")
# For each pair of rows of mat, test if they are independent.
r1 < rowChisqStats(mat)
# The values of Pearson's ChiSquare statistic as matrix.
r1$stats
# And the corresponding (unadjusted) pvalues.
r1$rawp
# Obtain only the values of the test statistic as vector
rowChisqStats(mat, compPval = FALSE, asMatrix =FALSE)
# Generate an example data set consisting of 10 rows (variables)
# and 200 columns (observations) by randomly drawing integers
# between 1 and 3, and a vector of class labels of length 200
# indicating that the first 100 observation belong to class 1
# and the other 100 to class 2.
mat2 < matrix(sample(3, 2000, TRUE), 10)
cl < rep(1:2, e = 100)
# For each row of mat2, test if they are associated with cl.
r2 < rowChisqStats(mat2, cl)
r2$stats
# And the results are identical to the one of chisq.test
pv < stat < numeric(10)
for(i in 1:10){
tmp < chisq.test(mat2[i,], cl)
pv[i] < tmp$p.value
stat[i] < tmp$stat
}
all.equal(r2$stats, stat)
all.equal(r2$rawp, pv)
## End(Not run)

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