pvalueschamm: Calculate the p_values matrix for each species, using Hamming...

In cmahalanobis: Calculate Distance Measures for a Given List of Data Frames with Factors

Calculate the p_values matrix for each species, using Hamming distance as a base.

Description

This function takes a dataset, a factor, a p_value method, number of bootstraps and permutation when necessary, and returns a p_values matrix between each pair of species and a plot if the user select TRUE using Hamming distance for the distances calculation.

Usage

pvalueschamm(
  dataset,
  formula,
  pvalue.method = "chisq",
  num.permutations = 100,
  num.bootstraps = 10,
  plot = TRUE
)

Arguments

dataset	A dataframe.
formula	A factor which you want to calculate Hamming distance.
pvalue.method	A p_value method used to calculate the matrix, the default value is "chisq". Other methods are "permutation" and "bootstrap".
num.permutations	Number of permutation to specify if you select "permutation" in "pvalue.method". The default value is 100.
num.bootstraps	Number of bootstrap to specify if you select "bootstrap" in "p_value method". The default value is 10.
plot	if TRUE, plot the p_values heatmap. The default value is TRUE.

Value

A list containing a matrix of p_values and, optionally, the plot.

Examples

# Calculate p_values of "Species" variable in iris dataset
pvalueschamm(iris,~Species, pvalue.method = "chisq", num.permutations = 100, num.bootstraps = 10)
# Calculate p_values of "am" variable in mtcars dataset
pvalueschamm(mtcars,~am, pvalue.method = "chisq", num.permutations = 100, num.bootstraps = 10)

cmahalanobis documentation built on April 3, 2025, 8:51 p.m.