Pairwise tests of independence for nominal data with matrix output
pairwiseNominalMatrix.RdConducts pairwise tests for a 2-dimensional matrix, in which at at least one dimension has more than two levels, as a post-hoc test. Conducts Fisher exact, Chi-square, or G-test.
Usage
pairwiseNominalMatrix(
x,
compare = "row",
fisher = TRUE,
gtest = FALSE,
chisq = FALSE,
method = "fdr",
correct = "none",
digits = 3,
...
)Arguments
- x
A two-way contingency table. At least one dimension should have more than two levels.
- compare
If
"row", treats the rows as the grouping variable. If"column", treats the columns as the grouping variable.- fisher
If
"TRUE", conducts fisher exact test.- gtest
If
"TRUE", conducts G-test.- chisq
If
"TRUE", conducts Chi-square test of association.- method
The method to adjust multiple p-values. See
p.adjust.- correct
The correction method to pass to
DescTools::GTest.- digits
The number of significant digits in the output.
- ...
Additional arguments, passed to
stats::fisher.test,DescTools::GTest, orstats::chisq.test.
Value
A list consisting of: the test used, a matrix of unadjusted p-values, the p-value adjustment method used, and a matrix of adjusted p-values.
Author
Salvatore Mangiafico, mangiafico@njaes.rutgers.edu
Examples
### Independence test for a 4 x 2 matrix
data(Anderson)
fisher.test(Anderson)
#>
#> Fisher's Exact Test for Count Data
#>
#> data: Anderson
#> p-value = 0.000668
#> alternative hypothesis: two.sided
#>
Anderson = Anderson[(c("Heimlich", "Bloom", "Dougal", "Cobblestone")),]
PT = pairwiseNominalMatrix(Anderson,
fisher = TRUE,
gtest = FALSE,
chisq = FALSE)$Adjusted
PT
#> Heimlich Bloom Dougal Cobblestone
#> Heimlich 1.00000 0.7400 0.0262 0.00596
#> Bloom 0.74000 1.0000 0.0564 0.01190
#> Dougal 0.02620 0.0564 1.0000 0.74000
#> Cobblestone 0.00596 0.0119 0.7400 1.00000
library(multcompView)
multcompLetters(PT)
#> Heimlich Bloom Dougal Cobblestone
#> "a" "ab" "bc" "c"