rcompanion (version 2.2.2)

pairwiseMcnemar: Pairwise McNemar and related tests for Cochran Q test post-hoc

Description

Conducts pairwise McNemar, exact, and permutation tests as a post-hoc to Cochran Q test.

Usage

pairwiseMcnemar(formula = NULL, data = NULL, x = NULL, g = NULL,
  block = NULL, test = "exact", method = "fdr", digits = 3,
  correct = FALSE)

Arguments

formula

A formula indicating the measurement variable and the grouping variable. e.g. y ~ group | block.

data

The data frame to use.

x

The response variable.

g

The grouping variable.

block

The blocking variable.

test

If "exact", conducts an exact test of symmetry analogous to a McNemar test. If "mcnemar", conducts a McNemar test of symmetry. If "permutation", conducts a permutation test analogous to a McNemar test.

method

The method for adjusting multiple p-values. See p.adjust.

digits

The number of significant digits in the output.

correct

If TRUE, applies a continuity correction for the McNemar test.

Value

A list containing: a data frame of results of the global test; a data frame of results of the pairwise results; and a data frame mentioning the p-value adjustment method.

Details

The component tables for the pairwise tests must be of size 2 x 2.

The input should include either formula and data; or x, g, and block.

References

http://rcompanion.org/handbook/H_07.html

See Also

nominalSymmetryTest, groupwiseCMH, pairwiseNominalIndependence, pairwiseNominalMatrix

Examples

# NOT RUN {
### Cochran Q post-hoc example
data(HayleySmith)
library(DescTools)
CochranQTest(Response ~ Practice | Student,
             data = HayleySmith)
HayleySmith$Practice = factor(HayleySmith$Practice,
                          levels = c("MowHeight", "SoilTest",
                                     "Clippings", "Irrigation"))
PT = pairwiseMcnemar(Response ~ Practice | Student,
                     data    = HayleySmith,
                     test    = "exact",
                     method  = "fdr",
                     digits  = 3)
PT
PT = PT$Pairwise
cldList(comparison = PT$Comparison,
        p.value    = PT$p.adjust,
        threshold  = 0.05)
                                                             
# }