| 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
 [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 |
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 |
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 |
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 |
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:
-
pca_weighted_eigen: This is the default method in FactoMineR to compute eigvalues, eigvectors and U matrix. -
pca_eigen: This is the standard method to compute eigenvalues, eigenvectors.
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 |
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 |
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:
-
standardize: standardization is performed by centering the data matrix and dividing by the square root of the sum of squares of the weights. This is the same method used inFactoMineR::PCA(). -
standardize_norm: standardization is performed by centering and scaling the data matrix. (X - µ) / S, where µ is the mean and S is the standard deviation.
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 |
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 |
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 |
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()