rcompanion (version 1.13.2)

pairwiseNominalIndependence: 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",
  cramer = FALSE, 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 GTest.

cramer

If "TRUE", includes and effect size, Cramer's V in the output.

digits

The number of significant digits in the output.

...

Additional arguments, passed to fisher.test, GTest, or chisq.test.

Value

A data frame of comparisons, p-values, and adjusted p-values.

References

http://rcompanion.org/handbook/H_04.html

See Also

pairwiseMcnemar, groupwiseCMH, nominalSymmetryTest, pairwiseNominalMatrix

Examples

# NOT RUN {
### 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)                             
                                                              
# }