dimensio0.14.1 package

Multivariate Data Analysis

biplot

Biplot

bootstrap

Partial Bootstrap Analysis

BootstrapCA-class

Bootstrap CA Results

BootstrapPCA-class

Bootstrap PCA Results

burt

Burt Table

CA-class

CA Results

ca

Correspondence Analysis

cdt

Complete Disjunctive Table

describe

Object Description

dimensio-defunct

Defunct Functions in dimensio

dimensio-deprecated

Deprecated Functions in dimensio

dimensio-package

dimensio: Multivariate Data Analysis

dimnames

Dimnames of an Object

export

Export Results

get_contributions

Get Contributions

get_coordinates

Get Coordinates

get_data

Get Original Data

get_eigenvalues

Get Eigenvalues

label

Non-Overlapping Text Labels

MCA-class

MCA Results

mca

Multiple Correspondence Analysis

MultivariateAnalysis

Output of Multivariate Data Analysis

MultivariateBootstrap

Output of Bootstrap Replications

MultivariateResults

Multivariate Data Analysis Results

MultivariateSummary

Summary of Multivariate Data Analysis

PCA-class

PCA Results

pca

Principal Components Analysis

PCOA-class

PCoA Results

pcoa

Principal Coordinates Analysis

plot

Plot Coordinates

predict

Predict New Coordinates

prepare_legend

Build a Legend

prepare_plot

Prepare Data for Plotting

reexports

Objects exported from other packages

screeplot

Scree Plot

subset

Extract Parts of an Object

summary

Object Summaries

svd2

Singular Value Decomposition of a Matrix

tidy

Tidy Coordinates

viz_confidence

Confidence Ellipses

viz_contributions

Visualize Contributions and cos2

viz_ellipses

Ellipses

viz_hull

Convex Hulls

viz_individuals

Visualize Individuals Factor Map

viz_labels

Non-Overlapping Text Labels

viz_legend

Add Legend

viz_points

Build a Factor Map

viz_tolerance

Tolerance Ellipses

viz_variables

Visualize Variables Factor Map

Simple Principal Components Analysis (PCA) and (Multiple) Correspondence Analysis (CA) based on the Singular Value Decomposition (SVD). This package provides S4 classes and methods to compute, extract, summarize and visualize results of multivariate data analysis. It also includes methods for partial bootstrap validation described in Greenacre (1984, ISBN: 978-0-12-299050-2) and Lebart et al. (2006, ISBN: 978-2-10-049616-7).

  • Maintainer: Nicolas Frerebeau
  • License: GPL (>= 3)
  • Last published: 2025-09-03