pairwiseNominalIndependence {rcompanion} | R Documentation |
Pairwise tests of independence for nominal data
Description
Conducts 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
pairwiseNominalIndependence(
x,
compare = "row",
fisher = TRUE,
gtest = TRUE,
chisq = TRUE,
method = "fdr",
correct = "none",
yates = FALSE,
stats = FALSE,
cramer = FALSE,
digits = 3,
...
)
Arguments
x |
A two-way contingency table. At least one dimension should have more than two levels. |
compare |
If |
fisher |
If |
gtest |
If |
chisq |
If |
method |
The method to adjust multiple p-values.
See |
correct |
The correction method to pass to |
yates |
Passed to |
stats |
If |
cramer |
If |
digits |
The number of significant digits in the output. |
... |
Additional arguments, passed to |
Value
A data frame of comparisons, p-values, and adjusted p-values.
Acknowledgments
My thanks to Carole Elliott of Kings Park & Botanic Gardens for suggesting the inclusion on the chi-square statistic and degrees of freedom in the output.
Author(s)
Salvatore Mangiafico, mangiafico@njaes.rutgers.edu
References
https://rcompanion.org/handbook/H_04.html
See Also
pairwiseMcnemar
, groupwiseCMH
,
nominalSymmetryTest
,
pairwiseNominalMatrix
Examples
### Independence test for a 4 x 2 matrix
data(Anderson)
fisher.test(Anderson)
Anderson = Anderson[(c("Heimlich", "Bloom", "Dougal", "Cobblestone")),]
PT = pairwiseNominalIndependence(Anderson,
fisher = TRUE,
gtest = FALSE,
chisq = FALSE,
cramer = TRUE)
PT
cldList(comparison = PT$Comparison,
p.value = PT$p.adj.Fisher,
threshold = 0.05)