Package {cmahalanobis}

Type:	Package
Title:	Calculate Distance Measures for DataFrames
Version:	1.1.0
Date:	2026-08-30
Maintainer:	Flavio Gioia <flaviogioia.fg@gmail.com>
Description:	It provides functions that calculate Mahalanobis distance, Euclidean distance, Manhattan distance, Chebyshev distance, Hamming distance, Canberra distance, Minkowski dissimilarity (distance defined for p >= 1), Cosine dissimilarity, Bhattacharyya dissimilarity, Jaccard distance, Hellinger distance, Bray-Curtis dissimilarity, Sorensen-Dice dissimilarity between each pair of species in a list of data frames. These statistics are fundamental in various fields, such as cluster analysis, classification, and other applications of machine learning and data mining, where assessing similarity or dissimilarity between data is crucial. The package is designed to be flexible and easily integrated into data analysis workflows, providing reliable tools for evaluating distances in multidimensional contexts.
License:	GPL-3
Encoding:	UTF-8
Imports:	stats, graphics, matrixStats
Suggests:	rmarkdown, testthat (≥ 3.0.0)
URL:	https://github.com/Hflavio12/cmahalanobis
NeedsCompilation:	no
Author:	Flavio Gioia [aut, cre]
Repository:	CRAN
Config/roxygen2/version:	8.0.0
Packaged:	2026-08-30 12:49:22 UTC; 50053539
Date/Publication:	2026-08-31 15:00:09 UTC

Calculate the Bhattacharyya dissimilarities for each pair of factors or for the index.

Description

This function takes a dataframe and a variable or variables (two or more) in input, and returns a matrix or matrices (two or more) with the Bhattacharyya dissimilarities about the factors inside them. You can also select "index" to calculate the Bhattacharyya dissimilarities between each row.

Usage

cbhattacharyya(
  dataset,
  formula,
  plot = TRUE,
  min_group_size = 3,
  method = "average",
  max_index_sample = NULL,
  automatic_encoding = FALSE,
  na_removal = FALSE,
  grouping_stat = "mean"
)

Arguments

Value

According to the option chosen in formula, with "index" the Bhattacharyya dissimilarities matrix will be printed; instead, by specifying variables, the Bhattacharyya dissimilarities matrix or matrices (two or more) between each pair of groups and, optionally, the plot or plots (two or more) will be printed.

Note

If "index" is selected with variables, only dissimilarities between rows are calculated. Therefore, this snippet: "cbhattacharyya(mtcars, ~am + carb + index)" will print dissimilarities only considering "index". Optionally, rows with NA values can be omitted and negative values are transformed in 0.

Examples

# Example with the CO2 dataset

table(CO2$Plant)

cbhattacharyya(CO2, ~Plant, 
   plot = TRUE, 
   grouping_stat = 'median', 
   automatic_encoding = TRUE)

# Example with the airquality dataset

summary(airquality)

cbhattacharyya(airquality, ~index, 
   plot = TRUE, 
   na_removal = TRUE, 
   max_index_sample = 40)

Calculate the Bray-Curtis dissimilarities for each pair of factors or for the index.

Description

This function takes a dataframe and a variable or variables (two or more) in input, and returns a matrix or matrices (two or more) with the Bray-Curtis dissimilarities about the factors inside them. You can also select "index" to calculate the Bray-Curtis dissimilarities between each row.

Usage

cbraycurtis(
  dataset,
  formula,
  plot = TRUE,
  min_group_size = 3,
  method = "average",
  max_index_sample = NULL,
  automatic_encoding = FALSE,
  na_removal = FALSE,
  grouping_stat = "mean"
)

Arguments

Value

According to the option chosen in formula, with "index" the Bray-Curtis dissimilarities matrix will be printed; instead, by specifying variables, the Bray-Curtis dissimilarities matrix or matrices (two or more) between each pair of factors and, optionally, the plot or plots (two or more) will be printed.

Note

If "index" is selected with variables, only dissimilarities between rows are calculated. Therefore, this snippet: "cbraycurtis(mtcars, ~am + carb + index)" will print dissimilarities only considering "index". Optionally, rows with NA values are omitted.

Examples

# Example with the CO2 dataset

table(CO2$Plant)

cbraycurtis(CO2, ~Plant, 
              plot = TRUE, 
              grouping_stat = 'mean', 
              na_removal = TRUE, 
              automatic_encoding = TRUE)

# Example with the airquality dataset

summary(airquality)

cbraycurtis(airquality, ~index, 
   plot = TRUE, 
   na_removal = TRUE, 
   max_index_sample = 40)

Calculate the Canberra distances for each pair of factors or for the index.

Description

This function takes a dataframe and a variable or variables (two or more) in input, and returns a matrix or matrices (two or more) with the Canberra distances about the factors inside them. You can also select "index" to calculate the Canberra distances between each row.

Usage

ccanberra(
  dataset,
  formula,
  plot = TRUE,
  min_group_size = 3,
  method = "average",
  max_index_sample = NULL,
  automatic_encoding = FALSE,
  na_removal = FALSE,
  grouping_stat = "mean"
)

Arguments

Value

According to the option chosen in formula, with "index" the Canberra distances matrix will be printed; instead, by specifying variables, the Canberra distances matrix or matrices (two or more) between each pair of groups and, optionally, the plot or plots (two or more) will be printed.

Note

If "index" is selected with variables, only distances between rows are calculated. Therefore, this snippet: "ccanberra(mtcars, ~am + carb + index)" will print distances only considering "index". Optionally, rows with NA values are omitted.

Examples

# Example with the CO2 dataset

table(CO2$Plant)

ccanberra(CO2, ~Plant, 
   plot = TRUE, 
   grouping_stat = 'median', 
   automatic_encoding = TRUE)

# Example with the airquality dataset

summary(airquality)

ccanberra(airquality, ~index, 
   plot = TRUE, 
   na_removal = TRUE, 
   max_index_sample = 40)

Calculate the Chebyshev distances for each pair of factors or for the index.

Description

This function takes a dataframe and a variable or variables (two or more) in input, and returns a matrix or matrices (two or more) with the Chebyshev distances about the factors inside them. You can also select "index" to calculate the Chebyshev distances between each row.

Usage

cchebyshev(
  dataset,
  formula,
  plot = TRUE,
  min_group_size = 3,
  method = "average",
  max_index_sample = NULL,
  automatic_encoding = FALSE,
  na_removal = FALSE,
  grouping_stat = "mean"
)

Arguments

Value

According to the option chosen in formula, with "index" the Chebyshev distances matrix will be printed; instead, by specifying variables, the Chebyshev distances matrix or matrices (two or more) between each pair of groups and, optionally, the plot or plots (two or more) will be printed.

Note

If "index" is selected with variables, only distances between rows are calculated. Therefore, this snippet: "cchebyshev(mtcars, ~am + carb + index)" will print distances only considering "index". Optionally, rows with NA values are omitted.

Examples

# Example with the CO2 dataset

table(CO2$Plant)

cchebyshev(CO2, ~Plant, 
              plot = TRUE, 
              grouping_stat = 'mean', 
              na_removal = TRUE, 
              automatic_encoding = TRUE)

# Example with the airquality dataset

summary(airquality)

cchebyshev(airquality, ~index, 
   plot = TRUE, 
   na_removal = TRUE, 
   max_index_sample = 40)

Calculate the Cosine dissimilarities for each pair of factors or for the index.

Description

This function takes a dataframe and a variable or variables (two or more) in input, and returns a matrix or matrices (two or more) with the Cosine dissimilarity about the factors inside them. You can also select "index" to calculate the Cosine distances between each row.

Usage

ccosine(
  dataset,
  formula,
  plot = TRUE,
  min_group_size = 3,
  method = "average",
  max_index_sample = NULL,
  automatic_encoding = FALSE,
  na_removal = FALSE,
  grouping_stat = "mean"
)

Arguments

Value

According to the option chosen in formula, with "index" the Cosine dissimilarity matrix will be printed; instead, by specifying variables, the Cosine dissimilarities matrix or matrices (two or more) between each pair of groups and, optionally, the plot or plots (two or more) will be printed.

Note

If "index" is selected with variables, only distances between rows are calculated. Therefore, this snippet: "ccosine(mtcars, ~am + carb + index)" will print distances only considering "index". Optionally, rows with NA values are omitted.

Examples

# Example with the CO2 dataset

table(CO2$Plant)

ccosine(CO2, ~Plant, 
   plot = TRUE, 
   grouping_stat = 'median', 
   automatic_encoding = TRUE)

# Example with the airquality dataset

summary(airquality)

ccosine(airquality, ~index, 
   plot = TRUE, 
   na_removal = TRUE, 
   max_index_sample = 40)
   

Calculate the Euclidean distances for each pair of factors or for the index.

Description

This function takes a dataframe and a variable or variables (two or more) in input, and returns a matrix or matrices (two or more) with the Euclidean distances about each pair of factors inside them. You can also select "index" to calculate the Euclidean distances between each row.

Usage

ceuclide(
  dataset,
  formula,
  plot = TRUE,
  min_group_size = 3,
  method = "average",
  max_index_sample = NULL,
  automatic_encoding = FALSE,
  na_removal = FALSE,
  grouping_stat = "mean"
)

Arguments

Value

According to the option chosen in formula, with "index" the Euclidean distance matrix will be printed; instead, by specifying variables, the Euclidean distances matrix or matrices (two or more) between each pair of groups and, optionally, the plot or plots (two or more) will be printed.

Note

If "index" is selected with variables, only distances between rows are calculated. Therefore, this snippet: "ceuclide(mtcars, ~am + carb + index)" will print distances only considering "index". Optionally, rows with missing values are omitted.

Examples

# Example with the CO2 dataset

table(CO2$Plant)

ceuclide(CO2, ~Plant, 
              plot = TRUE, 
              grouping_stat = 'mean', 
              na_removal = TRUE, 
              automatic_encoding = TRUE)

# Example with the airquality dataset

summary(airquality)

ceuclide(airquality, ~index, 
   plot = TRUE, 
   na_removal = TRUE, 
   max_index_sample = 40)

Calculate the Hamming distances for each pair of factors or for the index.

Description

This function takes a dataframe and a variable or variables (two or more) in input, and returns a matrix or matrices (two or more) with the Hamming distances about the factors inside them. You can also select "index" to calculate the Hamming distances between each row.

Usage

chamming(
  dataset,
  formula,
  plot = TRUE,
  min_group_size = 3,
  method = "average",
  max_index_sample = NULL,
  automatic_encoding = FALSE,
  na_removal = FALSE,
  grouping_stat = "mean"
)

Arguments

Value

According to the option chosen in formula, with "index" the Hamming distances matrix will be printed; instead, by specifying variables, the Hamming distances matrix or matrices (two or more) between each pair of factors and, optionally, the plot or plots (two or more) will be printed.

Note

If "index" is selected with variables, only distances between rows are calculated. Therefore, this snippet: "chamming(mtcars, ~am + carb + index)" will print the distances only considering "index". Optionally, rows with NA values are omitted.

Examples

# Example with the CO2 dataset

table(CO2$Plant)

chamming(CO2, ~Plant, 
   plot = TRUE, 
   grouping_stat = 'median', 
   automatic_encoding = TRUE)

# Example with the airquality dataset

summary(airquality)

chamming(airquality, ~index, 
   plot = TRUE, 
   na_removal = TRUE, 
   max_index_sample = 40)

Calculate the Hellinger distances for each pair of factors or for the index.

Description

This function takes a dataframe and a variable or variables (two or more) in input, and returns a matrix or matrices (two or more) with the Hellinger distances about the factors inside them. You can also select "index" to calculate the Hellinger distances between each row.

Usage

chellinger(
  dataset,
  formula,
  plot = TRUE,
  min_group_size = 3,
  method = "average",
  max_index_sample = NULL,
  automatic_encoding = FALSE,
  na_removal = FALSE,
  grouping_stat = "mean"
)

Arguments

Value

According to the option chosen in formula, with "index" the Hellinger distances matrix will be printed; instead, by specifying variables, the Hellinger distances matrix or matrices (two or more) between each pair of groups and, optionally, the plot or plots (two or more) will be printed.

Note

If "index" is selected with variables, only distances between rows are calculated. Therefore, this snippet: "chellinger(mtcars, ~am + carb + index)" will print the distances only considering "index". Optionally, rows with NA values are omitted.

Examples

# Example with the CO2 dataset

table(CO2$Plant)

chellinger(CO2, ~Plant, 
              plot = TRUE, 
              grouping_stat = 'mean', 
              na_removal = TRUE, 
              automatic_encoding = TRUE)

# Example with the airquality dataset

summary(airquality)

chellinger(airquality, ~index, 
   plot = TRUE, 
   na_removal = TRUE, 
   max_index_sample = 40)

Calculate the Jaccard distances for each pair of factors or for the index.

Description

This function takes a dataframe and a variable or variables (two or more) in input, and returns a matrix or matrices (two or more) with the Jaccard distance about the factors inside them. You can also select "index" to calculate the Jaccard distances between each row.

Usage

cjaccard(
  dataset,
  formula,
  plot = TRUE,
  min_group_size = 3,
  method = "average",
  max_index_sample = NULL,
  automatic_encoding = FALSE,
  na_removal = FALSE,
  grouping_stat = "mean"
)

Arguments

Value

According to the option chosen in formula, with "index" the Jaccard distances matrix will be printed; instead, by specifying variables, the Jaccard distances matrix or matrices (two or more) between each pair of groups and, optionally, the plot or plots (two or more) will be printed.

Note

If "index" is selected with variables, only dissimilarities between rows are calculated. Therefore, this snippet: "cjaccard(mtcars, ~am + carb + index)" will print dissimilarities only considering "index". Optionally, rows with NA values are omitted.

Examples

# Example with the CO2 dataset

table(CO2$Plant)

cjaccard(CO2, ~Plant, 
              plot = TRUE, 
              grouping_stat = 'mean', 
              na_removal = TRUE, 
              automatic_encoding = TRUE)

# Example with the airquality dataset

summary(airquality)

cjaccard(airquality, ~index, 
   plot = TRUE, 
   na_removal = TRUE, 
   max_index_sample = 40)

Calculate the Mahalanobis distances for each pair of factors or for the index.

Description

This function takes a dataframe and a variable or variables (two or more) in input, and returns a matrix or matrices (two or more) with the Mahalanobis distances about each pair of factors inside them. You can also select "index" to calculate the Mahalanobis distances between each row.

Usage

cmahalanobis(
  dataset,
  formula,
  plot = TRUE,
  min_group_size = 3,
  method = "average",
  pvalues_chisq = TRUE,
  max_index_sample = NULL,
  automatic_encoding = FALSE,
  na_removal = FALSE,
  grouping_stat = "mean"
)

Arguments

Value

According to the option chosen in formula and in pvalues_chisq, with "index" and "pvalues_chisq = TRUE" the squared Mahalanobis distance matrix will be printed with corresponding pvalues; instead, with "index" and "pvalues_chisq = FALSE", only the Mahalanobis distances (not squared) will be printed. By specifying variables, the Mahalanobis distances matrix or matrices (two or more) between each pair of factors and, optionally, the plot or plots (two or more) will be printed.

Note

If "index" is selected with variables, only distances between rows are calculated. Therefore, this snippet: "cmahalanobis(mtcars, ~am + carb + index)" will print distances and plot only considering "index". Optionally, rows with NA values are omitted.

Examples

# Example with the CO2 dataset

table(CO2$Plant)

cmahalanobis(CO2, ~Plant, pvalues_chisq = TRUE, 
              plot = TRUE, 
              grouping_stat = 'mean', 
              na_removal = TRUE, 
              automatic_encoding = TRUE)

# Example with the airquality dataset

summary(airquality)

cmahalanobis(airquality, ~index, 
   plot = TRUE, 
   na_removal = TRUE, 
   max_index_sample = 40)
   

Calculate the Manhattan distances for each pair of factors or for the index.

Description

This function takes a dataframe and a variable or variables (two or more) in input, and returns a matrix or matrices (two or more) with the Manhattan distances about the factors inside them. You can also select "index" to calculate the Manhattan distances between each row.

Usage

cmanhattan(
  dataset,
  formula,
  plot = TRUE,
  min_group_size = 3,
  method = "average",
  max_index_sample = NULL,
  automatic_encoding = FALSE,
  na_removal = FALSE,
  grouping_stat = "mean"
)

Arguments

Value

According to the option chosen in formula, with "index" the Manhattan distances matrix will be printed; instead, by specifying variables, the Manhattan distances matrix or matrices (two or more) between each pair of groups and, optionally, the plot or plots (two or more) will be printed.

Note

If "index" is selected with variables, only distances between rows are calculated. Therefore, this snippet: "cmanhattan(mtcars, ~am + carb + index)" will print the distances only considering "index". Optionally, rows with NA values are omitted.

Examples

# Example with the CO2 dataset

table(CO2$Plant)

cmanhattan(CO2, ~Plant, 
   plot = TRUE, 
   grouping_stat = 'median', 
   automatic_encoding = TRUE)

# Example with the airquality dataset

summary(airquality)

cmanhattan(airquality, ~index, 
   plot = TRUE, 
   na_removal = TRUE, 
   max_index_sample = 40)

Calculate the Minkowski distances for each pair of factors or for the index.

Description

This function takes a dataframe and a variable or variables (two or more) in input, and returns a matrix or matrices (two or more) with the Minkowski distances about the factors inside them. You can also select "index" to calculate the Minkowski distances between each row.

Usage

cminkowski(
  dataset,
  formula,
  p = 3,
  plot = TRUE,
  min_group_size = 3,
  method = "average",
  max_index_sample = NULL,
  automatic_encoding = FALSE,
  na_removal = FALSE,
  grouping_stat = "mean"
)

Arguments

Value

According to the option chosen in formula, with "index" the Minkowski distances matrix will be printed; instead, by specifying variables, the Minkowski distances matrix or matrices (two or more) between each pair of groups and, optionally, the plot or plots (two or more) will be printed.

Note

When p < 1, the Minkowski distance is a "dissimilarity" measure. When p >= 1, the triangle inequality property is satisfied and we say "Minkowski distance". If "index" is selected with variables, only distances between rows are calculated. Therefore, this snippet: "cminkowski(mtcars, ~am + carb + index)" will print distances only considering "index". Optionally, rows with NA values are omitted.

Examples

# Example with the CO2 dataset

table(CO2$Plant)

cminkowski(CO2, ~Plant, p = 4, 
              plot = TRUE, 
              grouping_stat = 'mean', 
              na_removal = TRUE, 
              automatic_encoding = TRUE)

# Example with the airquality dataset

summary(airquality)

cminkowski(airquality, ~index, p = 3,
   plot = TRUE, 
   na_removal = TRUE, 
   max_index_sample = 40)

Calculate the Sorensen-Dice dissimilarities for each pair of factors or for the index.

Description

This function takes a dataframe and a variable or variables (two or more) in input, and returns a matrix or matrices (two or more) with the Sorensen-Dice dissimilarities about the factors inside them. You can also select "index" to calculate the Sorensen-Dice dissimilarities between each row.

Usage

csorensendice(
  dataset,
  formula,
  plot = TRUE,
  min_group_size = 3,
  method = "average",
  max_index_sample = NULL,
  automatic_encoding = FALSE,
  na_removal = FALSE,
  grouping_stat = "mean"
)

Arguments

Value

According to the option chosen in formula, with "index" the Sorensen-Dice dissimilarities matrix will be printed; instead, by specifying variables, the Sorensen-Dice dissimilarities matrix or matrices (two or more) between each pair of groups and, optionally, the plot or plots (two or more) will be printed.

Note

If "index" is selected with variables, only dissimilarities between rows are calculated. Therefore, this snippet: "csorensendice(mtcars, ~am + carb + index)" will print dissimilarities only considering "index". Optionally, rows with NA values are omitted.

Examples

# Example with the CO2 dataset

table(CO2$Plant)

csorensendice(CO2, ~Plant, 
   plot = TRUE, 
   grouping_stat = 'median', 
   automatic_encoding = TRUE)

# Example with the airquality dataset

summary(airquality)

csorensendice(airquality, ~index, 
   plot = TRUE, 
   na_removal = TRUE, 
   max_index_sample = 40)

Generate a Microsoft Word document about the Bhattacharyya dissimilarities matrix or matrices (two or more) and the p-values matrix or matrices (two or more).

Description

This function takes a dataframe, a factor or factors (two or more) and returns a Microsoft Word document about the Bhattacharyya dissimilarities matrix or matrices (two or more) and the p-values matrix or matrices (two or more).

Usage

generate_report_cbhattacharyya(
  dataset,
  formula,
  pvalue.method = "permutation",
  seed = NULL,
  min_group_size = 3,
  num_replicas = 1000,
  automatic_encoding = TRUE,
  na_removal = TRUE,
  grouping_stat = "mean"
)

Arguments

Value

A Microsoft Word document about the Bhattacharyya dissimilarities matrix or matrices (two or more) and the p_values matrix or matrices (two or more).

Note

About pvalues, this function leverages on an early stopping procedure in which the resulting matrix is printed also if the specified number of replicas is not reached; if every 500 replicas the maximum difference between each p_value does not exceed 0.0001, the function will print the entire matrix, else it continues.

Examples

generate_report_cbhattacharyya(CO2, ~Plant + Type,
   pvalue.method = "permutation",
   seed = 122,
   num_replicas = 10,
   grouping_stat = 'median', 
   automatic_encoding = TRUE)
   
generate_report_cbhattacharyya(airquality, ~Ozone,
   pvalue.method = 'bootstrap',
   na_removal = TRUE, num_replicas = 10)

Generate a Microsoft Word document about the Bray-Curtis dissimilarities matrix or matrices (two or more) and the p-values matrix or matrices (two or more).

Description

This function takes a dataframe, a factor or factors (two or more) and returns a Microsoft Word document about the Bray-Curtis dissimilarities matrix or matrices (two or more) and the p-values matrix or matrices (two or more).

Usage

generate_report_cbraycurtis(
  dataset,
  formula,
  pvalue.method = "permutation",
  seed = NULL,
  min_group_size = 3,
  num_replicas = 1000,
  automatic_encoding = TRUE,
  na_removal = TRUE,
  grouping_stat = "mean"
)

Arguments

Value

A Microsoft Word document about the Bray-Curtis dissimilarities matrix or matrices (two or more) and the p_values matrix or matrices (two or more).

Note

About pvalues, this function leverages on an early stopping procedure in which the resulting matrix is printed also if the specified number of replicas is not reached; if every 500 replicas the maximum difference between each p_value does not exceed 0.0001, the function will print the entire matrix, else it continues.

Examples

generate_report_cbraycurtis(CO2, ~Plant + Type,
   pvalue.method = "permutation",
   seed = 122,
   num_replicas = 10,
   grouping_stat = 'median', 
   automatic_encoding = TRUE)

generate_report_cbraycurtis(airquality, ~Ozone,
   pvalue.method = 'bootstrap',
   na_removal = TRUE, num_replicas = 10)

Generate a Microsoft Word document about the Canberra distances matrix or matrices (two or more) and the p-values matrix or matrices (two or more).

Description

This function takes a dataframe, a factor or factors (two or more) and returns a Microsoft Word document about the Canberra distances matrix or matrices (two or more) and the p-values matrix or matrices (two or more).

Usage

generate_report_ccanberra(
  dataset,
  formula,
  pvalue.method = "permutation",
  seed = NULL,
  min_group_size = 3,
  num_replicas = 1000,
  automatic_encoding = TRUE,
  na_removal = TRUE,
  grouping_stat = "mean"
)

Arguments

Value

A Microsoft Word document about the Canberra distances matrix or matrices (two or more) and the p_values matrix or matrices (two or more).

Note

About pvalues, this function leverages on an early stopping procedure in which the resulting matrix is printed also if the specified number of replicas is not reached; if every 500 replicas the maximum difference between each p_value does not exceed 0.0001, the function will print the entire matrix, else it continues.

Examples

generate_report_ccanberra(CO2, ~Plant + Type,
   pvalue.method = "permutation",
   seed = 122,
   num_replicas = 10,
   grouping_stat = 'median', 
   automatic_encoding = TRUE)

generate_report_ccanberra(airquality, ~Ozone,
   pvalue.method = 'bootstrap',
   na_removal = TRUE, num_replicas = 10)

Generate a Microsoft Word document about the Chebyshev distances matrix or matrices (two or more) and the p-values matrix or matrices (two or more).

Description

This function takes a dataframe, a factor or factors (two or more) and returns a Microsoft Word document about the Chebyshev distances matrix or matrices (two or more) and the p-values matrix or matrices (two or more).

Usage

generate_report_cchebyshev(
  dataset,
  formula,
  pvalue.method = "permutation",
  seed = NULL,
  min_group_size = 3,
  num_replicas = 1000,
  automatic_encoding = TRUE,
  na_removal = TRUE,
  grouping_stat = "mean"
)

Arguments

Value

A Microsoft Word document about the Chebyshev distances matrix or matrices (two or more) and the p_values matrix or matrices (two or more).

Note

About pvalues, this function leverages on an early stopping procedure in which the resulting matrix is printed also if the specified number of replicas is not reached; if every 500 replicas the maximum difference between each p_value does not exceed 0.0001, the function will print the entire matrix, else it continues.

Examples

generate_report_cchebyshev(CO2, ~Plant + Type,
   pvalue.method = "permutation",
   seed = 122,
   num_replicas = 10,
   grouping_stat = 'median', 
   automatic_encoding = TRUE)

generate_report_cchebyshev(airquality, ~Ozone,
   pvalue.method = 'bootstrap',
   na_removal = TRUE, num_replicas = 10)

Generate a Microsoft Word document about the Cosine dissimilarities matrix or matrices (two or more) and the p-values matrix or matrices (two or more).

Description

This function takes a dataframe, a factor or factors (two or more) and returns a Microsoft Word document about the Cosine distances matrix or matrices (two or more) and the p-values matrix or matrices (two or more).

Usage

generate_report_ccosine(
  dataset,
  formula,
  pvalue.method = "permutation",
  seed = NULL,
  min_group_size = 3,
  num_replicas = 1000,
  automatic_encoding = TRUE,
  na_removal = TRUE,
  grouping_stat = "mean"
)

Arguments

Value

A Microsoft Word document about the Cosine distances matrix or matrices (two or more) and the p_values matrix or matrices (two or more).

Note

About pvalues, this function leverages on an early stopping procedure in which the resulting matrix is printed also if the specified number of replicas is not reached; if every 500 replicas the maximum difference between each p_value does not exceed 0.0001, the function will print the entire matrix, else it continues.

Examples

generate_report_ccosine(CO2, ~Plant + Type,
   pvalue.method = "permutation",
   seed = 122,
   num_replicas = 10,
   grouping_stat = 'median', 
   automatic_encoding = TRUE)

generate_report_ccosine(airquality, ~Ozone,
   pvalue.method = 'bootstrap',
   na_removal = TRUE, num_replicas = 10)

Generate a Microsoft Word document about the Euclidean distances matrix or matrices and the p-values matrix or matrices.

Description

This function takes a dataframe, a factor or factors (two or more) and returns a Microsoft Word document about the Euclidean distances matrix or matrices (two or more) and the p-values matrix or matrices (two or more).

Usage

generate_report_ceuclide(
  dataset,
  formula,
  pvalue.method = "permutation",
  seed = NULL,
  min_group_size = 3,
  num_replicas = 1000,
  automatic_encoding = TRUE,
  na_removal = TRUE,
  grouping_stat = "mean"
)

Arguments

Value

A Microsoft Word document about the Euclidean distances matrix or matrices (two or more) and the p_values matrix or matrices (two or more).

Note

About pvalues, this function leverages on an early stopping procedure in which the resulting matrix is printed also if the specified number of replicas is not reached; if every 500 replicas the maximum difference between each p_value does not exceed 0.0001, the function will print the entire matrix, else it continues.

Examples

generate_report_ceuclide(CO2, ~Plant + Type,
   pvalue.method = "permutation",
   seed = 122,
   num_replicas = 10,
   grouping_stat = 'median', 
   automatic_encoding = TRUE)

generate_report_ceuclide(airquality, ~Ozone,
   pvalue.method = 'bootstrap',
   na_removal = TRUE, num_replicas = 10)

Generate a Microsoft Word document about the Hamming distances matrix or matrices (two or more) and the p-values matrix or matrices (two or more).

Description

This function takes a dataframe, a factor or factors (two or more) and returns a Microsoft Word document about the Hamming distances matrix or matrices (two or more) and the p-values matrix or matrices (two or more).

Usage

generate_report_chamming(
  dataset,
  formula,
  pvalue.method = "permutation",
  seed = NULL,
  min_group_size = 3,
  num_replicas = 1000,
  automatic_encoding = TRUE,
  na_removal = TRUE,
  grouping_stat = "mean"
)

Arguments

Value

A Microsoft Word document about the Hamming distances matrix or matrices (two or more) and the p_values matrix or matrices (two or more).

Note

About pvalues, this function leverages on an early stopping procedure in which the resulting matrix is printed also if the specified number of replicas is not reached; if every 500 replicas the maximum difference between each p_value does not exceed 0.0001, the function will print the entire matrix, else it continues.

Examples

generate_report_chamming(CO2, ~Plant + Type,
   pvalue.method = "permutation",
   seed = 122,
   num_replicas = 10,
   grouping_stat = 'median', 
   automatic_encoding = TRUE)

generate_report_chamming(airquality, ~Ozone,
   pvalue.method = 'bootstrap',
   na_removal = TRUE, num_replicas = 10)

Generate a Microsoft Word document about the Hellinger distances matrix or matrices (two or more) and the p-values matrix or matrices (two or more).

Description

This function takes a dataframe, a factor or factors (two or more) and returns a Microsoft Word document about the Hellinger distances matrix or matrices (two or more) and the p-values matrix or matrices (two or more).

Usage

generate_report_chellinger(
  dataset,
  formula,
  pvalue.method = "permutation",
  seed = NULL,
  min_group_size = 3,
  num_replicas = 1000,
  automatic_encoding = TRUE,
  na_removal = TRUE,
  grouping_stat = "mean"
)

Arguments

Value

A Microsoft Word document about the Hellinger distances matrix or matrices (two or more) and the p_values matrix or matrices (two or more).

Note

About pvalues, this function leverages on an early stopping procedure in which the resulting matrix is printed also if the specified number of replicas is not reached; if every 500 replicas the maximum difference between each p_value does not exceed 0.0001, the function will print the entire matrix, else it continues.

Examples

 generate_report_chellinger(CO2, ~Plant + Type,
   pvalue.method = "permutation",
   seed = 122,
   num_replicas = 10,
   grouping_stat = 'median', 
   automatic_encoding = TRUE)
 
 generate_report_chellinger(airquality, ~Ozone,
   pvalue.method = 'bootstrap',
   na_removal = TRUE, num_replicas = 10)

Generate a Microsoft Word document about the Jaccard distances matrix or matrices (two or more) and the p-values matrix or matrices (two or more).

Description

This function takes a dataframe, a factor and returns a Microsoft Word document about the Jaccard distances matrix or matrices and the p-values matrix or matrices.

Usage

generate_report_cjaccard(
  dataset,
  formula,
  pvalue.method = "permutation",
  seed = NULL,
  min_group_size = 3,
  num_replicas = 1000,
  automatic_encoding = TRUE,
  na_removal = TRUE,
  grouping_stat = "mean"
)

Arguments

Value

A Microsoft Word document about the Jaccard distance matrix or matrices and the p_values matrix or matrices.

Note

About pvalues, this function leverages on an early stopping procedure in which the resulting matrix is printed also if the specified number of replicas is not reached; if every 500 replicas the maximum difference between each p_value does not exceed 0.0001, the function will print the entire matrix, else it continues.

Examples

generate_report_cjaccard(CO2, ~Plant + Type,
   pvalue.method = "permutation",
   seed = 122,
   num_replicas = 10,
   grouping_stat = 'median', 
   automatic_encoding = TRUE)

generate_report_cjaccard(airquality, ~Ozone,
   pvalue.method = 'bootstrap',
   na_removal = TRUE, num_replicas = 10)

Generate a Microsoft Word document about the Mahalanobis distances matrix or matrices and the p-values matrix or matrices.

Description

This function takes a dataframe, a factor or factors (two or more) and returns a Microsoft Word document about the Mahalanobis distances matrix or matrices (two or more) and the p-values matrix or matrices.

Usage

generate_report_cmahalanobis(
  dataset,
  formula,
  pvalue.method = "permutation",
  seed = NULL,
  min_group_size = 3,
  pvalues_chisq = FALSE,
  num_replicas = 1000,
  automatic_encoding = TRUE,
  na_removal = TRUE,
  grouping_stat = "mean"
)

Arguments

Value

A Microsoft Word document about the Mahalanobis distances matrix or matrices (two or more) and the p_values matrix or matrices (two or more).

Note

About pvalues, this function leverages on an early stopping procedure in which the resulting matrix is printed also if the specified number of replicas is not reached; if every 500 replicas the maximum difference between each p_value does not exceed 0.0001, the function will print the entire matrix, else it continues.

Examples

generate_report_cmahalanobis(CO2, ~Plant + Type,
   pvalues_chisq = TRUE,
   pvalue.method = "permutation",
   seed = 122,
   num_replicas = 10,
   grouping_stat = 'median', 
   automatic_encoding = TRUE)

generate_report_cmahalanobis(airquality, ~Ozone,
   pvalues_chisq = FALSE,
   pvalue.method = 'bootstrap',
   na_removal = TRUE, num_replicas = 10)
 

Generate a Microsoft Word document about the Manhattan distances matrix or matrices (two or more) and the p-values matrix or matrices (two or more).

Description

This function takes a dataframe, a factor or factors (two or more) and returns a Microsoft Word document about the Manhattan distances matrix or matrices (two or more) and the p-values matrix or matrices (two or more).

Usage

generate_report_cmanhattan(
  dataset,
  formula,
  pvalue.method = "permutation",
  seed = NULL,
  min_group_size = 3,
  num_replicas = 1000,
  automatic_encoding = TRUE,
  na_removal = TRUE,
  grouping_stat = "mean"
)

Arguments

Value

A Microsoft Word document about the Manhattan distances matrix or matrices (two or more) and the p_values matrix or matrices (two or more).

Note

About pvalues, this function leverages on an early stopping procedure in which the resulting matrix is printed also if the specified number of replicas is not reached; if every 500 replicas the maximum difference between each p_value does not exceed 0.0001, the function will print the entire matrix, else it continues.

Examples

generate_report_cmanhattan(CO2, ~Plant + Type,
   pvalue.method = "permutation",
   seed = 122,
   num_replicas = 10,
   grouping_stat = 'median', 
   automatic_encoding = TRUE)

generate_report_cmanhattan(airquality, ~Ozone,
   pvalue.method = 'bootstrap',
   na_removal = TRUE, num_replicas = 10)

Generate a Microsoft Word document about the Minkowski dissimilarities/distances matrix or matrices (two or more) and the p-values matrix or matrices (two or more).

Description

This function takes a dataframe, a factor or factors (two or more) and returns a Microsoft Word document about the Minkowski dissimilarities/distances matrix or matrices (two or more) and the p-values matrix or matrices (two or more).

Usage

generate_report_cminkowski(
  dataset,
  formula,
  p = 3,
  pvalue.method = "permutation",
  seed = NULL,
  min_group_size = 3,
  num_replicas = 1000,
  automatic_encoding = TRUE,
  na_removal = TRUE,
  grouping_stat = "mean"
)

Arguments

Details

When p < 1, the Minkowski distance is a "dissimilarity" measure. When p >= 1, the triangle inequality property is satisfied and we say "Minkowski distance".

Value

A Microsoft Word document about the Minkowski dissimilarities/distances matrix or matrices (two or more) and the p_values matrix or matrices (two or more).

Note

About pvalues, this function leverages on an early stopping procedure in which the resulting matrix is printed also if the specified number of replicas is not reached; if every 500 replicas the maximum difference between each p_value does not exceed 0.0001, the function will print the entire matrix, else it continues.

Examples

generate_report_cminkowski(CO2, ~Plant + Type,
   p = 3,
   pvalue.method = "permutation",
   seed = 122,
   num_replicas = 10,
   grouping_stat = 'median', 
   automatic_encoding = TRUE)

generate_report_cminkowski(airquality, ~Ozone,
   p = 4,
   pvalue.method = 'bootstrap',
   na_removal = TRUE, num_replicas = 10)
   

Generate a Microsoft Word document about the Sorensen-Dice dissimilarity matrix or matrices (two or more) and the p-values matrix or matrices (two or more).

Description

This function takes a dataframe, a factor or factors (two or more) and returns a Microsoft Word document about the Sorensen-Dice dissimilarities matrix or matrices (two or more) and the p-values matrix or matrices (two or more).

Usage

generate_report_csorensendice(
  dataset,
  formula,
  pvalue.method = "permutation",
  seed = NULL,
  min_group_size = 3,
  num_replicas = 1000,
  automatic_encoding = TRUE,
  na_removal = TRUE,
  grouping_stat = "mean"
)

Arguments

Value

A Microsoft Word document about the Sorensen-Dice dissimilarities matrix or matrices (two or more) and the p_values matrix or matrices (two or more).

Note

About pvalues, this function leverages on an early stopping procedure in which the resulting matrix is printed also if the specified number of replicas is not reached; if every 500 replicas the maximum difference between each p_value does not exceed 0.0001, the function will print the entire matrix, else it continues.

Examples

generate_report_csorensendice(CO2, ~Plant + Type,
   pvalue.method = "permutation",
   seed = 122,
   num_replicas = 10,
   grouping_stat = 'median', 
   automatic_encoding = TRUE)

generate_report_csorensendice(airquality, ~Ozone,
   pvalue.method = 'bootstrap',
   na_removal = TRUE, num_replicas = 10)

Calculate the p_values matrix or matrices (two or more) for each factor inside variable or variables (two or more), using Bhattacharyya dissimilarities as a base.

Description

Using the Bhattacharyya dissimilarity for the dissimilarities calculation, this function takes a dataframe, a variable or variables (two or more), a p_value method such as "bootstrap" and "permutation" and returns the p_values matrix or matrices (two or more) between each pair of factors and a plot or plots (two or more) if the user select TRUE or leaves the parameter without argument.

Usage

pvaluescbatt(
  dataset,
  formula,
  pvalue.method = "permutation",
  seed = NULL,
  min_group_size = 3,
  num_replicas = 1000,
  automatic_encoding = FALSE,
  na_removal = FALSE,
  grouping_stat = "mean"
)

Arguments

Value

A list containing a matrix or matrices (two or more) of p_values and, optionally, the plot.

Note

This function leverages on an early stopping procedure in which the resulting matrix is printed also if the specified number of replicas is not reached; if every 500 replicas the maximum difference between each p_value does not exceed 0.0001, the function will print the entire matrix, else it continues.

Examples

pvaluescbatt(CO2, ~Plant + Type,
   pvalue.method = "permutation",
   seed = 122,
   num_replicas = 50,
   grouping_stat = 'median', 
   automatic_encoding = TRUE)

pvaluescbatt(airquality, ~Ozone,
   pvalue.method = 'bootstrap',
   na_removal = TRUE,
   grouping_stat = 'mean', num_replicas = 50)

Calculate the p_values matrix or matrices (two or more) for each factor inside variable or variables (two or more), using Bray-Curtis dissimilarity as a base.

Description

Using the Bray-Curtis dissimilarity for the dissimilarities calculation, this function takes a dataframe, a variable or variables (two or more), a p_value method such as "bootstrap" and "permutation" and returns the p_values matrix or matrices (two or more) between each pair of factors and a plot or plots (two or more) if the user select TRUE or leaves the parameter without argument.

Usage

pvaluescbrcu(
  dataset,
  formula,
  pvalue.method = "permutation",
  seed = NULL,
  min_group_size = 3,
  num_replicas = 1000,
  automatic_encoding = FALSE,
  na_removal = FALSE,
  grouping_stat = "mean"
)

Arguments

Value

A list containing a matrix or matrices (two or more) of p_values and, optionally, the plot.

Note

This function leverages on an early stopping procedure in which the resulting matrix is printed also if the specified number of replicas is not reached; if every 500 replicas the maximum difference between each p_value does not exceed 0.0001, the function will print the entire matrix, else it continues.

Examples

pvaluescbrcu(CO2, ~Plant + Type,
   pvalue.method = "permutation",
   seed = 122,
   num_replicas = 50,
   grouping_stat = 'median', 
   automatic_encoding = TRUE)

pvaluescbrcu(airquality, ~Ozone,
   pvalue.method = 'bootstrap',
   na_removal = TRUE, num_replicas = 50)

Calculate the p_values matrix or matrices (two or more) for each factor inside variable or variables (two or more), using Canberra distances as a base.

Description

Using the Canberra distance for the distances calculation, this function takes a dataframe, a variable or variables (two or more), a p_value method such as "bootstrap" and "permutation" and returns the p_values matrix or matrices (two or more) between each pair of factors and a plot or plots (two or more) if the user select TRUE or leaves the parameter without argument.

Usage

pvaluesccanb(
  dataset,
  formula,
  pvalue.method = "permutation",
  seed = NULL,
  min_group_size = 3,
  num_replicas = 1000,
  automatic_encoding = FALSE,
  na_removal = FALSE,
  grouping_stat = "mean"
)

Arguments

Value

A list containing a matrix or matrices (two or more) of p_values.

Note

This function leverages on an early stopping procedure in which the resulting matrix is printed also if the specified number of replicas is not reached; if every 500 replicas the maximum difference between each p_value does not exceed 0.0001, the function will print the entire matrix, else it continues.

Examples

pvaluesccanb(CO2, ~Plant + Type,
    pvalue.method = "permutation",
    seed = 122,
    num_replicas = 50,
    grouping_stat = 'median', 
    automatic_encoding = TRUE)

pvaluesccanb(airquality, ~Ozone,
    pvalue.method = 'bootstrap',
    na_removal = TRUE, num_replicas = 50)

Calculate the p_values matrix or matrices (two or more) for each factor inside variable or variables (two or more), using Chebyshev distances as a base.

Description

Using the Chebyshev distance for the distances calculation, this function takes a dataframe, a variable or variables (two or more), a p_value method such as "bootstrap" and "permutation" and returns the p_values matrix or matrices (two or more) between each pair of factors and a plot or plots (two or more) if the user select TRUE or leaves the parameter without argument.

Usage

pvaluesccheb(
  dataset,
  formula,
  pvalue.method = "permutation",
  seed = NULL,
  min_group_size = 3,
  num_replicas = 1000,
  automatic_encoding = FALSE,
  na_removal = FALSE,
  grouping_stat = "mean"
)

Arguments

Value

A list containing a matrix or matrices (two or more) of p_values.

#' @note This function leverages on an early stopping procedure in which the resulting matrix is printed also if the specified number of replicas is not reached; if every 500 replicas the maximum difference between each p_value does not exceed 0.0001, the function will print the entire matrix, else it continues.

Examples

pvaluesccheb(CO2, ~Plant + Type,
     pvalue.method = "permutation",
     seed = 122,
     num_replicas = 50,
     grouping_stat = 'median', 
     automatic_encoding = TRUE)
     
pvaluesccheb(airquality, ~Ozone,
     pvalue.method = 'bootstrap',
     na_removal = TRUE, num_replicas = 50)

Calculate the p_values matrix or matrices (two or more) for each factor inside variable or variables (two or more), using Cosine dissimilarities as a base.

Description

Using the Cosine distance, this function takes a dataframe, a variable or variables (two or more), a p_value method such as "bootstrap" and "permutation" and returns the p_values matrix or matrices (two or more) between each pair of factors and a plot or plots (two or more) if the user select TRUE or leaves the parameter without argument.

Usage

pvaluesccosi(
  dataset,
  formula,
  pvalue.method = "permutation",
  seed = NULL,
  min_group_size = 3,
  num_replicas = 1000,
  automatic_encoding = FALSE,
  na_removal = FALSE,
  grouping_stat = "mean"
)

Arguments

Value

A list containing a matrix or matrices (two or more) of p_values and, optionally, the plot.

Note

This function leverages on an early stopping procedure in which the resulting matrix is printed also if the specified number of replicas is not reached; if every 500 replicas the maximum difference between each p_value does not exceed 0.0001, the function will print the entire matrix, else it continues.

Examples

pvaluesccosi(CO2, ~Plant + Type,
   pvalue.method = "permutation",
   seed = 122,
   num_replicas = 50,
   grouping_stat = 'median', 
   automatic_encoding = TRUE)
   
pvaluesccosi(airquality, ~Ozone,
   pvalue.method = 'bootstrap',
   na_removal = TRUE, num_replicas = 50)

Calculate the p_values matrix or matrices (two or more) for each pair of factors inside variable or variables (two or more), using Euclidean distances as a base.

Description

Using the Euclidean distance for the distances calculation, this function takes a dataframe, a variable or variables (two or more), a p_value method such as "bootstrap" and "permutation" and returns the p_values matrix or matrices (two or more) between each pair of factors and a plot or plots (two or more) if the user select TRUE or leaves the parameter without argument.

Usage

pvaluesceucl(
  dataset,
  formula,
  pvalue.method = "permutation",
  seed = NULL,
  min_group_size = 3,
  num_replicas = 1000,
  automatic_encoding = FALSE,
  na_removal = FALSE,
  grouping_stat = "mean"
)

Arguments

Value

A list containing a matrix or matrices (two or more) of p_values.

#' @note This function leverages on an early stopping procedure in which the resulting matrix is printed also if the specified number of replicas is not reached; if every 500 replicas the maximum difference between each p_value does not exceed 0.0001, the function will print the entire matrix, else it continues.

Examples

pvaluesceucl(CO2, ~Plant + Type,
   pvalue.method = "permutation",
   seed = 122,
   num_replicas = 50,
   grouping_stat = 'median', 
   automatic_encoding = TRUE)
   
pvaluesceucl(airquality, ~Ozone,
   pvalue.method = 'bootstrap',
   na_removal = TRUE, num_replicas = 50)

Calculate the p_values matrix or matrices (two or more) for each factor inside variable or variables (two or more), using Hamming distances as a base.

Description

Using the Hamming distance for the distances calculation, this function takes a dataframe, a variable or variables (two or more), a p_value method such as "bootstrap" and "permutation" and returns the p_values matrix or matrices (two or more) between each pair of factors.

Usage

pvalueschamm(
  dataset,
  formula,
  pvalue.method = "permutation",
  seed = NULL,
  min_group_size = 3,
  num_replicas = 1000,
  automatic_encoding = FALSE,
  na_removal = FALSE,
  grouping_stat = "median"
)

Arguments

Value

A list containing a matrix or matrices (two or more) of p_values.

Note

This function leverages on an early stopping procedure in which the resulting matrix is printed also if the specified number of replicas is not reached; if every 500 replicas the maximum difference between each p_value does not exceed 0.0001, the function will print the entire matrix, else it continues.

Examples

pvalueschamm(CO2, ~Plant + Type,
   pvalue.method = "permutation",
   seed = 122,
   num_replicas = 50,
   grouping_stat = 'median', 
   automatic_encoding = TRUE)
   
   
pvalueschamm(airquality, ~Ozone,
   pvalue.method = 'bootstrap',
   na_removal = TRUE, num_replicas = 50)

Calculate the p_values matrix or matrices (two or more) for each factor inside variable or variables (two or more), using Hellinger distances as a base.

Description

Using the Hellinger distance for the distances calculation, this function takes a dataframe, a variable or variables (two or more), a p_value method such as "bootstrap" and "permutation" and returns the p_values matrix or matrices (two or more) between each pair of factors and a plot or plots (two or more) if the user select TRUE or leaves the parameter without argument.

Usage

pvalueschell(
  dataset,
  formula,
  pvalue.method = "permutation",
  seed = NULL,
  min_group_size = 3,
  num_replicas = 1000,
  automatic_encoding = FALSE,
  na_removal = FALSE,
  grouping_stat = "mean"
)

Arguments

Value

A list containing a matrix or matrices (two or more) of p_values and, optionally, the plot.

Note

This function leverages on an early stopping procedure in which the resulting matrix is printed also if the specified number of replicas is not reached; if every 500 replicas the maximum difference between each p_value does not exceed 0.0001, the function will print the entire matrix, else it continues.

Examples

pvalueschell(CO2, ~Plant + Type,
   pvalue.method = "permutation",
   seed = 122,
   num_replicas = 50,
   grouping_stat = 'median', 
   automatic_encoding = TRUE)
   
pvalueschell(airquality, ~Ozone,
   pvalue.method = 'bootstrap',
   na_removal = TRUE, num_replicas = 50)

Calculate the p_values matrix or matrices (two or more) for each factor inside variable or variables (two or more), using Jaccard distances as a base.

Description

Using the Jaccard distance for the distance calculation, this function takes a dataframe, a variable or variables (two or more), a p_value method such as "bootstrap" and "permutation" and returns the p_values matrix or matrices (two or more) between each pair of factors and a plot or plots (two or more) if the user select TRUE or leaves the parameter without argument.

Usage

pvaluescjacc(
  dataset,
  formula,
  pvalue.method = "permutation",
  seed = NULL,
  min_group_size = 3,
  num_replicas = 1000,
  automatic_encoding = FALSE,
  na_removal = FALSE,
  grouping_stat = "mean"
)

Arguments

Value

A list containing a matrix or matrices (two or more) of p_values and, optionally, the plot.

Note

This function leverages on an early stopping procedure in which the resulting matrix is printed also if the specified number of replicas is not reached; if every 500 replicas the maximum difference between each p_value does not exceed 0.0001, the function will print the entire matrix, else it continues

Examples

pvaluescjacc(CO2, ~Plant + Type,
   pvalue.method = "permutation",
   seed = 122,
   num_replicas = 50,
   grouping_stat = 'median', 
   automatic_encoding = TRUE)
   
pvaluescjacc(airquality, ~Ozone,
   pvalue.method = 'bootstrap',
   na_removal = TRUE, num_replicas = 50)

Calculate the p_values matrix or matrices (two or more) for each pair of factors inside variable or variables (two or more), using Mahalanobis distances as a base.

Description

Using the Mahalanobis distance for the distances calculation, this function takes a dataframe, a variable or variables (two or more), a p_value method such as bootstrap" and "permutation" and returns the p_values matrix or matrices (two or more) between each pair of factors.

Usage

pvaluescmaha(
  dataset,
  formula,
  pvalue.method = "permutation",
  seed = NULL,
  min_group_size = 3,
  num_replicas = 1000,
  automatic_encoding = FALSE,
  na_removal = FALSE,
  grouping_stat = "median"
)

Arguments

Value

A list containing a matrix or matrices (two or more) of p_values.

Note

This function leverages on an early stopping procedure in which the resulting matrix is printed also if the specified number of replicas is not reached; if every 500 replicas the maximum difference between each p_value does not exceed 0.0001, the function will print the entire matrix, else it continues.

Examples

pvaluescmaha(CO2, ~Plant + Type,
   pvalue.method = "permutation",
   seed = 122,
   num_replicas = 50,
   grouping_stat = 'median', 
   automatic_encoding = TRUE)
   
pvaluescmaha(airquality, ~Ozone,
   pvalue.method = 'bootstrap',
   na_removal = TRUE, num_replicas = 50)

Calculate the p_values matrix or matrices (two or more) for each factor inside variable or variables (two or more), using Manhattan distances as a base.

Description

Using the Manhattan distance for the distances calculation, this function takes a dataframe, a variable or variables (two or more), a p_value method such as "bootstrap" and "permutation" and returns the p_values matrix or matrices (two or more) between each pair of factors and a plot or plots (two or more) if the user select TRUE or leaves the parameter without argument.

Usage

pvaluescmanh(
  dataset,
  formula,
  pvalue.method = "permutation",
  seed = NULL,
  min_group_size = 3,
  num_replicas = 1000,
  automatic_encoding = FALSE,
  na_removal = FALSE,
  grouping_stat = "median"
)

Arguments

Value

A list containing a matrix or matrices (two or more) of p_values.

Note

This function leverages on an early stopping procedure in which the resulting matrix is printed also if the specified number of replicas is not reached; if every 500 replicas the maximum difference between each p_value does not exceed 0.0001, the function will print the entire matrix, else it continues.

Examples

pvaluescmanh(CO2, ~Plant + Type,
   pvalue.method = "permutation",
   seed = 122,
   num_replicas = 50,
   grouping_stat = 'median', 
   automatic_encoding = TRUE)
   
pvaluescmanh(airquality, ~Ozone,
   pvalue.method = 'bootstrap',
   na_removal = TRUE, num_replicas = 50)

Calculate the p_values matrix or matrices (two or more) for each factor inside variable or variables (two or more), using Minkowski dissimilarities/distances as a base.

Description

Using the Minkowski dissimilarity/distance for the dissimilarities/distances calculation, this function takes a dataframe, a variable or variables (two or more), a p_value method such as "bootstrap" and "permutation" and returns the p_values matrix or matrices (two or more) between each pair of factors and a plot or plots (two or more) if the user select TRUE or leaves the parameter without argument.

Usage

pvaluescmink(
  dataset,
  formula,
  pvalue.method = "permutation",
  seed = NULL,
  min_group_size = 3,
  num_replicas = 1000,
  p = 3,
  automatic_encoding = FALSE,
  na_removal = FALSE,
  grouping_stat = "median"
)

Arguments

Value

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

Note

When p < 1, the Minkowski distance is a "dissimilarity" measure. When p >= 1, the triangle inequality property is satisfied and we say "Minkowski distance". This function leverages on an early stopping procedure in which the resulting matrix is printed also if the specified number of replicas is not reached; if every 500 replicas the maximum difference between each p_value does not exceed 0.0001, the function will print the entire matrix, else it continue.

Examples

pvaluescmink(CO2, ~Plant + Type,
   pvalue.method = "permutation", 
   seed = 122,
   num_replicas = 50,
   p = 3,
   grouping_stat = 'median', 
   automatic_encoding = TRUE)
   
 pvaluescmink(airquality, ~Ozone,
   pvalue.method = 'bootstrap',
   p = 3,
   na_removal = TRUE, num_replicas = 50)

Calculate the p_values matrix or matrices (two or more) for each factor inside variable or variables (two or more), using Sorensen-Dice dissimilarities as a base.

Description

Using the Sorensen-Dice dissimilarity for the dissimilarities calculation, this function takes a dataframe, a variable or variables (two or more), a p_value method such as "bootstrap" and "permutation" and returns the p_values matrix or matrices (two or more) between each pair of factors and a plot or plots (two or more) if the user select TRUE or leaves the parameter without argument.

Usage

pvaluescsore(
  dataset,
  formula,
  pvalue.method = "permutation",
  seed = NULL,
  min_group_size = 3,
  num_replicas = 1000,
  automatic_encoding = FALSE,
  na_removal = FALSE,
  grouping_stat = "mean"
)

Arguments

Value

A list containing a matrix or matrices (two or more) of p_values and, optionally, the plot.

Note

This function leverages on an early stopping procedure in which the resulting matrix is printed also if the specified number of replicas is not reached; if every 500 replicas the maximum difference between each p_value does not exceed 0.0001, the function will print the entire matrix, else it continues.

Examples

pvaluescsore(CO2, ~Plant + Type,
   pvalue.method = "permutation",
   seed = 122,
   num_replicas = 50,
   grouping_stat = 'median', 
   automatic_encoding = TRUE)
   
pvaluescsore(airquality, ~Ozone,
   pvalue.method = 'bootstrap',
   na_removal = TRUE, num_replicas = 50)