Pairwise permutation tests for percentiles

Conducts pairwise permutation tests across groups for percentiles, medians, and proportion below a threshold value.

Usage

pairwisePercentileTest(
  formula = NULL,
  data = NULL,
  x = NULL,
  y = NULL,
  test = "median",
  tau = 0.5,
  type = 7,
  threshold = NA,
  comparison = "<",
  r = 1000,
  digits = 4,
  progress = "TRUE",
  method = "fdr"
)

Arguments

formula: A formula indicating the response variable and the independent variable. e.g. y ~ group.
data: The data frame to use.
x: If no formula is given, the response variable for one group.
y: The response variable for the other group.
test: The statistic to compare between groups. Can be "median", "percentile", "iqr", "proportion", "mean", or "variance".
tau: If "percentile" is chosen as the test, tau indicates the percentile to test. Expressed as a quantile. That is, 0.5 indicates a test for medians. 0.75 indicates a test for 75th percentiles.
type: The type value passed to the quantile function.
threshold: If "proportion" is chosen as the test, threshold indicates the value of the dependent variable to use as the threshold. For example, to test if there is a different in the proportion of observations below $10,000, threshold = 10000 would be used.
comparison: If "proportion" is chosen as the test, comparison indicates the inequality to use. Options are "<", "<=", ">", ">=", or , "=="
r: The number of replicates in the permutation test.
digits: The number of significant digits in the output.
progress: If TRUE, prints a dot for every 1 percent of the progress while conducting the test.
method: The p-value adjustment method to use for multiple tests. See stats::p.adjust.

Value

A dataframe of the groups being compared, the p-values, and the adjusted p-values.

Details

The function conducts pairwise tests using the percentileTest function. The user can consult the documentation for that function for additional details.

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

Note

The parsing of the formula is simplistic. The first variable on the left side is used as the measurement variable. The first variable on the right side is used for the grouping variable.

References

https://rcompanion.org/handbook/F_15.html

Author

Salvatore Mangiafico, mangiafico@njaes.rutgers.edu

Examples

if (FALSE) { # \dontrun{
data(BrendonSmall)
PT = pairwisePercentileTest(Sodium ~ Instructor, 
                            data = BrendonSmall, 
                            test = "percentile", 
                            tau  = 0.75)
PT
cldList(p.adjust ~ Comparison,
        data       = PT,
        threshold  = 0.05)
        
data(BrendonSmall)
PT = pairwisePercentileTest(Sodium ~ Instructor, 
                            data       = BrendonSmall, 
                            test       = "proportion", 
                            threshold  = 1300)
PT
cldList(p.adjust ~ Comparison,
        data       = PT,
        threshold  = 0.05)                         
} # }