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Type: Package
Title: Multivariate Exploratory Data Analysis
Version: 1.0.0
Description: Exploratory data analysis methods to summarize, visualize and describe datasets. The main principal component methods are available, those with the largest potential in terms of applications: principal component analysis (PCA) when variables are quantitative, correspondence analysis (CA) when variables are categorical, Multiple Factor Analysis (MFA) when variables are structured in groups.
License: MIT + file LICENSE
URL: https://github.com/alexym1/booklet, https://alexym1.github.io/booklet/
BugReports: https://github.com/alexym1/booklet/issues
Depends: R (≥ 4.1.0)
Suggests: covr, devtools, factoextra, FactoMineR, knitr, renv, testthat
VignetteBuilder: knitr
Encoding: UTF-8
RoxygenNote: 7.3.2
NeedsCompilation: no
Packaged: 2025-04-20 13:19:25 UTC; amartinez
Author: Alex Yahiaoui Martinez ORCID iD [aut, cre]
Maintainer: Alex Yahiaoui Martinez <yahiaoui-martinez.alex@outlook.com>
Repository: CRAN
Date/Publication: 2025-04-24 07:10:02 UTC

Compute col contributions

Description

Return col contributions for each correspondence component

Usage

ca_col_contrib(col_coords, X, eigs)

Arguments

col_coords

col coordinates

X

standardized matrix

eigs

eigs computed by ca_weighted_eigen

Value

A dataframe of col contributions.

Examples

library(booklet)

X_scaled <- mtcars[, c(2, 8:11)] |>
  ca_standardize()

eigs <- X_scaled |>
  ca_weighted_eigen()

eigs |>
  ca_col_coords() |>
  ca_col_contrib(X_scaled, eigs) |>
  head()

Compute col coordinates

Description

Return Correspondence component for columns

Usage

ca_col_coords(eigs)

ca_col_sup_coords(X_sup, eigs)

Arguments

eigs

eigs computed by ca_weighted_eigen

X_sup

Supplementary dataset

Value

A dataframe of col coordinates.

Examples

library(booklet)

mtcars[, c(2, 8:11)] |>
  ca_standardize() |>
  ca_weighted_eigen() |>
  ca_col_coords() |>
  head()

Compute col squared cosines

Description

Return col squared cosines for each correspondence component

Usage

ca_col_cos2(col_coords, X)

ca_col_sup_cos2(col_coords, X_sup, X)

Arguments

col_coords

col coordinates

X

active dataset

X_sup

supplementary dataset

Value

A dataframe of col squared cosines.

Examples

library(booklet)

X_scaled <- mtcars[, c(2, 8:11)] |>
  ca_standardize()

X_scaled |>
  ca_weighted_eigen() |>
  ca_col_coords() |>
  ca_col_cos2(X_scaled) |>
  head()

Compute col inertia

Description

Return col inertia for each correspondence component

Usage

ca_col_inertia(X)

Arguments

X

standardized matrix

Value

A dataframe of col inertia.

Examples

library(booklet)

mtcars[, c(2, 8:11)] |>
  ca_standardize() |>
  ca_col_inertia()

Compute row contributions

Description

Return row contributions for each correspondence component

Usage

ca_row_contrib(row_coords, X, eigs)

Arguments

row_coords

row coordinates

X

standardized matrix

eigs

eigs computed by ca_weighted_eigen

Value

A dataframe of row contributions.

Examples

library(booklet)

X_scaled <- mtcars[, c(2, 8:11)] |>
  ca_standardize()

eigs <- X_scaled |>
  ca_weighted_eigen()

eigs |>
  ca_row_coords() |>
  ca_row_contrib(X_scaled, eigs) |>
  head()

Compute row coordinates

Description

Return Correspondence component for individuals

Usage

ca_row_coords(eigs)

ca_row_sup_coords(X_sup, eigs)

Arguments

eigs

eigs computed by ca_weighted_eigen

X_sup

Supplementary dataset

Value

A dataframe of row coordinates.

Examples

library(booklet)

mtcars[, c(2, 8:11)] |>
  ca_standardize() |>
  ca_weighted_eigen() |>
  ca_row_coords() |>
  head()

Compute row squared cosines

Description

Return row squared cosines for each correspondence component

Usage

ca_row_cos2(row_coords, X)

ca_row_sup_cos2(row_coords, X_sup, X)

Arguments

row_coords

row coordinates

X

Active standardized matrix

X_sup

Supplementary standardized matrix

Value

A dataframe of row squared cosines.

Examples

library(booklet)

X_scaled <- mtcars[, c(2, 8:11)] |>
  ca_standardize()

X_scaled |>
  ca_weighted_eigen() |>
  ca_row_coords() |>
  ca_row_cos2(X_scaled) |>
  head()

Compute row inertia

Description

Return row inertia for each correspondence component

Usage

ca_row_inertia(X)

Arguments

X

standardized matrix

Value

A dataframe of row inertia.

Examples

library(booklet)

mtcars[, c(2, 8:11)] |>
  ca_standardize() |>
  ca_row_inertia()

Data standardization for CA

Description

Perform data standardization for multivariate exploratory data analysis.

Usage

ca_standardize(X, weighted_row = rep(1, nrow(X)))

ca_standardize_sup(X, type = c("row", "col"), weighted_row = rep(1, nrow(X)))

Arguments

X

Active or supplementary datasets

weighted_row

row weights

type

standardization for supplementary rows or cols

Value

A dataframe of the same size as X.

Examples

library(booklet)

mtcars[, c(2, 8:11)] |>
  ca_standardize() |>
  head()

Compute eigenvalues and eigenvectors for CA

Description

Return eigenvalues and eigenvectors of a matrix

Usage

ca_weighted_eigen(X)

Arguments

X

X_active

Value

A list containing results of Single Value Decomposition (SVD).

Examples

library(booklet)

mtcars[, c(2, 8:11)] |>
  ca_standardize() |>
  ca_weighted_eigen() |>
  head()

Perform CA with FactoMineR's style

Description

Return CA results with FactoMineR's style

Usage

facto_ca(X, ncp = 5, row_sup = NULL, col_sup = NULL, weighted_row = NULL)

Arguments

X

a data frame with n rows (individuals) and p columns (numeric variables)

ncp

an integer, the number of components to keep (value set by default)

row_sup

a vector indicating the indexes of the supplementary rows

col_sup

a vector indicating the indexes of the supplementary cols

weighted_row

row weights

Value

A list containing results of FactoMineR's correspondence analysis (CA).

Examples

library(booklet)
res <- facto_ca(X = mtcars[, c(2, 8:11)], ncp = 2)

Perform MFA with FactoMineR's style

Description

Return MFA results with FactoMineR's style

Usage

facto_mfa(X, groups, ncp = 2)

Arguments

X

a data frame with n rows (individuals) and p columns (numeric variables)

groups

a vector indicating the group of each variable

ncp

an integer, the number of components to keep (value set by default)

Value

A list containing results of FactoMineR's multiple factor analysis (MFA).

Examples

library(booklet)

res <- facto_mfa(X = iris[, -c(5)], groups = c(2, 2), ncp = 2)

Perform PCA with FactoMineR's style

Description

Return PCA results with FactoMineR's style

Usage

facto_pca(
  X,
  ncp = 5,
  scale.unit = TRUE,
  ind_sup = NULL,
  quanti_sup = NULL,
  weighted_col = NULL
)

Arguments

X

a data frame with n rows (individuals) and p columns (numeric variables)

ncp

an integer, the number of components to keep (value set by default)

scale.unit

a boolean, if TRUE (value set by default) then data are scaled to unit variance

ind_sup

a vector indicating the indexes of the supplementary individuals

quanti_sup

a vector indicating the indexes of the quantitative supplementary variables

weighted_col

column weights

Value

A list containing results of FactoMineR's principal components analysis (PCA).

Examples

library(booklet)

res <- facto_pca(iris[, -5], ncp = 2, ind_sup = 1, quanti_sup = 1)

Compute eigenvalues and eigenvectors

Description

Return eigenvalues and eigenvectors of a matrix

Usage

pca_eigen(X)

pca_weighted_eigen(
  X,
  weighted_row = rep(1, nrow(X))/nrow(X),
  weighted_col = rep(1, ncol(X))
)

Arguments

X

X_active

weighted_row

row weights

weighted_col

column weights

Details

Standardization depends on what you need to perform factor analysis. We implemented two types:

Value

A list containing results of Single Value Decomposition (SVD).

Examples

library(booklet)

iris[, -5] |>
  pca_standardize_norm() |>
  pca_eigen()

Compute individual contributions

Description

Return individual contributions for each principal component

Usage

pca_ind_contrib(
  ind_coords,
  eigs,
  weighted_row = rep(1, nrow(ind_coords))/nrow(ind_coords)
)

Arguments

ind_coords

individual coordinates

eigs

eigs computed by pca_eigen or pca_weighted_eigen

weighted_row

row weights

Details

If you want to compute the contributions of the individuals to the principal components, you have to change the weighted_col argument to rep(1, nrow(ind_cos2)).

Value

A dataframe of individual contributions.

Examples

library(booklet)

eigs <- iris[, -5] |>
  pca_standardize_norm() |>
  pca_weighted_eigen()

eigs |>
  pca_ind_coords() |>
  pca_ind_contrib(eigs) |>
  head()

Compute coordinates for individuals

Description

Return principal component for individuals

Usage

pca_ind_coords(eigs)

Arguments

eigs

eigs computed by pca_eigen or pca_weighted_eigen

Value

A dataframe of individual coordinates.

Examples

library(booklet)

iris[, -5] |>
  pca_standardize_norm() |>
  pca_weighted_eigen() |>
  pca_ind_coords() |>
  head()

Compute individual squared cosines

Description

Return individual squared cosines for each principal component

Usage

pca_ind_cos2(ind_coords, weighted_col = rep(1, ncol(ind_coords)))

Arguments

ind_coords

individual coordinates

weighted_col

column weights

Value

A dataframe of individual squared cosines.

Examples

library(booklet)

iris[, -5] |>
  pca_standardize_norm() |>
  pca_weighted_eigen() |>
  pca_ind_coords() |>
  pca_ind_cos2() |>
  head()

Data standardization for PCA

Description

Perform data standardization for multivariate exploratory data analysis.

Usage

pca_standardize_norm(X, center = TRUE, scale = TRUE)

pca_standardize(X, scale = TRUE, weighted_row = rep(1, nrow(X))/nrow(X))

Arguments

X

matrix

center

centering by the mean

scale

scaling by the standard deviation

weighted_row

row weights

Details

Standardization depends on what you need to perform factor analysis. Two methods are implemented:

Value

A dataframe of the same size as X.

Examples

library(booklet)

iris[, -5] |>
  pca_standardize_norm() |>
  head()

Compute variable contributions

Description

Return variable contributions

Usage

pca_var_contrib(var_cos2, eigs, weighted_col = rep(1, ncol(var_cos2)))

Arguments

var_cos2

variable coordinates

eigs

eigs computed by pca_eigen or pca_weighted_eigen

weighted_col

column weights

Value

A dataframe of variable contributions.

Examples

library(booklet)

eigs <- iris[, -5] |>
  pca_standardize_norm() |>
  pca_weighted_eigen()

eigs |>
  pca_var_coords() |>
  pca_var_cos2() |>
  pca_var_contrib(eigs) |>
  head()

Compute variable coordinates

Description

Return variable coordinates

Usage

pca_var_coords(eigs)

Arguments

eigs

eigs computed by pca_eigen or pca_weighted_eigen

Value

A dataframe of variable coordinates.

Examples

library(booklet)

iris[, -5] |>
  pca_standardize_norm() |>
  pca_weighted_eigen() |>
  pca_var_coords() |>
  head()

Compute variable correlation

Description

Return variable correlation

Usage

pca_var_cor(eigs)

Arguments

eigs

eigs computed by pca_eigen or pca_weighted_eigen

Value

A dataframe of variable correlation.

Examples

library(booklet)

iris[, -5] |>
  pca_standardize_norm() |>
  pca_weighted_eigen() |>
  pca_var_cor() |>
  head()

Compute variable squared cosines

Description

Return variable squared cosines

Usage

pca_var_cos2(var_coords)

Arguments

var_coords

variable coordinates

Value

A dataframe of variable squared consines.

Examples

library(booklet)

iris[, -5] |>
  pca_standardize_norm() |>
  pca_weighted_eigen() |>
  pca_var_coords() |>
  pca_var_cos2() |>
  head()

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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