Title: Multivariate Data Analysis
Version: 0.14.0
Maintainer: Nicolas Frerebeau <nicolas.frerebeau@u-bordeaux-montaigne.fr>
Description: 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).
License: GPL (≥ 3)
URL: https://codeberg.org/tesselle/dimensio, https://packages.tesselle.org/dimensio/, https://tesselle.r-universe.dev/dimensio
BugReports: https://codeberg.org/tesselle/dimensio/issues
Depends: R (≥ 3.5)
Imports: arkhe (≥ 1.10.0), graphics, grDevices, khroma (≥ 1.16.0), methods, utils
Suggests: fontquiver, knitr, markdown, rsvg, svglite, tinysnapshot, tinytest
VignetteBuilder: knitr
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.3.2.9000
X-schema.org-isPartOf: https://www.tesselle.org
X-schema.org-keywords: data-analysis, multivariate-analysis, r-package
Collate: 'AllClasses.R' 'AllGenerics.R' 'annotations.R' 'augment.R' 'biplot.R' 'bootstrap.R' 'ca.R' 'coerce.R' 'data.R' 'dimensio-defunct.R' 'dimensio-deprecated.R' 'dimensio-internal.R' 'dimensio-package.R' 'export.R' 'get_contributions.R' 'get_coordinates.R' 'get_correlations.R' 'get_cos2.R' 'get_data.R' 'get_distances.R' 'get_eigenvalues.R' 'get_inertia.R' 'get_variance.R' 'mca.R' 'mutators.R' 'pca.R' 'pcoa.R' 'plot.R' 'predict.R' 'reexport.R' 'screeplot.R' 'show.R' 'subset.R' 'summary.R' 'svd.R' 'tidy.R' 'tools.R' 'viz_contributions.R' 'viz_coordinates.R' 'viz_cos2.R' 'viz_ellipse.R' 'viz_hull.R' 'wrap_ellipses.R' 'wrap_hull.R' 'zzz.R'
NeedsCompilation: no
Packaged: 2025-05-20 07:42:15 UTC; nfrerebeau
Author: Nicolas Frerebeau ORCID iD [aut, cre], Jean-Baptiste Fourvel ORCID iD [ctb], Camille Thabard ORCID iD [ctb], Brice Lebrun ORCID iD [art] (Logo designer), Université Bordeaux Montaigne ROR ID [fnd], CNRS ROR ID [fnd]
Repository: CRAN
Date/Publication: 2025-05-20 09:20:09 UTC

dimensio: Multivariate Data Analysis

Description

logo

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).

Details

Version 0.14.0
License GPL-3
CRAN DOI doi:10.32614/CRAN.package.dimensio
Zenodo DOI doi:10.5281/zenodo.4478530

Archéosciences Bordeaux (UMR 6034)
Maison de l'Archéologie
Université Bordeaux Montaigne
F-33607 Pessac cedex
France

Package options

dimensio uses the following options() to configure behavior:

Author(s)

Maintainer: Nicolas Frerebeau nicolas.frerebeau@u-bordeaux-montaigne.fr (ORCID)

Other contributors:

See Also

Useful links:

Bootstrap CA Results

Description

An S4 class to store the bootstrap of a correspondence analysis.

Note

This class inherits from CA and MultivariateBootstrap.

Author(s)

N. Frerebeau

See Also

Other class: BootstrapPCA-class, CA-class, MCA-class, MultivariateAnalysis, MultivariateBootstrap, MultivariateResults, MultivariateSummary, PCA-class, PCOA-class

Examples

## Bootstrap on CA
## Data from Lebart et al. 2006, p. 170-172
data("colours")

## Compute correspondence analysis
X <- ca(colours)

## Bootstrap (30 replicates)
Y <- bootstrap(X, n = 30)

## Not run: 
## Get replicated coordinates
get_replications(Y, margin = 1)
get_replications(Y, margin = 2)

## End(Not run)

## Plot with ellipses
viz_rows(Y)
viz_tolerance(Y, level = c(0.68, 0.95))

viz_columns(Y)
viz_tolerance(Y, level = c(0.68, 0.95))

## Plot with convex hulls
viz_columns(Y)
viz_hull(Y)

## Bootstrap on PCA
## Compute principal components analysis
data("iris")
X <- pca(iris, sup_quali = "Species")

## Bootstrap (30 replicates)
Y <- bootstrap(X, n = 30)

## Plot with ellipses
viz_variables(Y)
viz_tolerance(Y, level = c(0.68, 0.95))

Bootstrap PCA Results

Description

An S4 class to store the bootstrap of a principal components analysis.

Note

This class inherits from PCA and MultivariateBootstrap.

Author(s)

N. Frerebeau

See Also

Other class: BootstrapCA-class, CA-class, MCA-class, MultivariateAnalysis, MultivariateBootstrap, MultivariateResults, MultivariateSummary, PCA-class, PCOA-class

Examples

## Bootstrap on CA
## Data from Lebart et al. 2006, p. 170-172
data("colours")

## Compute correspondence analysis
X <- ca(colours)

## Bootstrap (30 replicates)
Y <- bootstrap(X, n = 30)

## Not run: 
## Get replicated coordinates
get_replications(Y, margin = 1)
get_replications(Y, margin = 2)

## End(Not run)

## Plot with ellipses
viz_rows(Y)
viz_tolerance(Y, level = c(0.68, 0.95))

viz_columns(Y)
viz_tolerance(Y, level = c(0.68, 0.95))

## Plot with convex hulls
viz_columns(Y)
viz_hull(Y)

## Bootstrap on PCA
## Compute principal components analysis
data("iris")
X <- pca(iris, sup_quali = "Species")

## Bootstrap (30 replicates)
Y <- bootstrap(X, n = 30)

## Plot with ellipses
viz_variables(Y)
viz_tolerance(Y, level = c(0.68, 0.95))

CA Results

Description

An S4 class to store the results of a simple correspondence analysis.

Note

This class inherits from MultivariateAnalysis.

Author(s)

N. Frerebeau

See Also

Other class: BootstrapCA-class, BootstrapPCA-class, MCA-class, MultivariateAnalysis, MultivariateBootstrap, MultivariateResults, MultivariateSummary, PCA-class, PCOA-class

Examples

## Data from Lebart et al. 2006, p. 170-172
data("colours")

## The chi square of independence between the two variables
stats::chisq.test(colours)

## Compute correspondence analysis
X <- ca(colours)

## Plot rows
viz_rows(X, labels = TRUE)

## Plot columns
viz_columns(X, labels = TRUE)

## Get row coordinates
get_coordinates(X, margin = 1)

## Get column coordinates
get_coordinates(X, margin = 2)

## Get total inertia
sum(get_inertia(X))

## Get row contributions
get_contributions(X, margin = 1)

MCA Results

Description

An S4 class to store the results of a multiple correspondence analysis.

Note

This class inherits from CA.

Author(s)

N. Frerebeau

See Also

Other class: BootstrapCA-class, BootstrapPCA-class, CA-class, MultivariateAnalysis, MultivariateBootstrap, MultivariateResults, MultivariateSummary, PCA-class, PCOA-class

Output of Multivariate Data Analysis

Description

A virtual S4 class to store the output of a multivariate data analysis.

Slots

data

A numeric matrix.

dimension

An integer giving the dimension of the solution.

singular_values

A numeric vector giving the singular values.

rows

A MultivariateResults object.

columns

A MultivariateResults object.

extra

A list of extra variables.

Subset

In the code snippets below, x is a MultivariateAnalysis object.

x[[i]]

Extracts information from a slot selected by subscript i. i is a length-one character vector.

Author(s)

N. Frerebeau

See Also

Other class: BootstrapCA-class, BootstrapPCA-class, CA-class, MCA-class, MultivariateBootstrap, MultivariateResults, MultivariateSummary, PCA-class, PCOA-class

Output of Bootstrap Replications

Description

A virtual S4 class to store the output of a bootstrap analysis.

Slots

replications

An integer giving the number of bootstrap replications.

Author(s)

N. Frerebeau

See Also

Other class: BootstrapCA-class, BootstrapPCA-class, CA-class, MCA-class, MultivariateAnalysis, MultivariateResults, MultivariateSummary, PCA-class, PCOA-class

Multivariate Data Analysis Results

Description

An S4 class to store the results of a multivariate data analysis.

Slots

names

A character vector specifying the row names.

principal

A numeric matrix giving the principal coordinates.

standard

A numeric matrix giving the standard coordinates.

contributions

A numeric matrix giving the contributions to the definition of the dimensions.

cosine

A numeric matrix giving the cos^2 values.

distances

A numeric vector giving the distances to centroid.

weights

A numeric vector giving the masses/weights.

supplement

A logical vector specifying the supplementary points.

order

An integer vector giving the original indices of the data (computation moves all supplementary points at the end of the results).

groups

A character vector specifying the class for each observation.

Author(s)

N. Frerebeau

See Also

Other class: BootstrapCA-class, BootstrapPCA-class, CA-class, MCA-class, MultivariateAnalysis, MultivariateBootstrap, MultivariateSummary, PCA-class, PCOA-class

Summary of Multivariate Data Analysis

Description

A virtual S4 class to store the summary of a multivariate data analysis.

Slots

data

A numeric matrix.

eigenvalues

A numeric matrix.

results

A numeric matrix.

supplement

A logical vector specifying the supplementary points.

margin

An integer.

Author(s)

N. Frerebeau

See Also

Other class: BootstrapCA-class, BootstrapPCA-class, CA-class, MCA-class, MultivariateAnalysis, MultivariateBootstrap, MultivariateResults, PCA-class, PCOA-class

PCA Results

Description

An S4 class to store the results of a principal components analysis.

Slots

center

A numeric vector giving the column mean of the initial dataset (active individuals only).

scale

A numeric vector giving the column standard deviations of the initial dataset (active individuals only).

Note

This class inherits from MultivariateAnalysis.

Author(s)

N. Frerebeau

See Also

Other class: BootstrapCA-class, BootstrapPCA-class, CA-class, MCA-class, MultivariateAnalysis, MultivariateBootstrap, MultivariateResults, MultivariateSummary, PCOA-class

Examples

## Load data
data("iris")

## Compute principal components analysis
X <- pca(iris, sup_quali = "Species")

## Get eigenvalues
get_eigenvalues(X)

## Get individual cos2
head(get_cos2(X, margin = 1))

## Get variable contributions
get_contributions(X, margin = 2)

## Get correlations between variables and dimensions
get_correlations(X)

PCoA Results

Description

An S4 class to store the results of a principal coordinates analysis.

Slots

points

A numeric matrix whose rows give the coordinates of the points chosen to represent the dissimilarities.

eigenvalues

A numeric vector giving the eigenvalues computed during the scaling process.

method

A character string giving the distance that has been used to create the distance structure.

GOF

A length-two numeric vector.

groups

A character vector specifying the class for each observation.

Author(s)

N. Frerebeau

See Also

Other class: BootstrapCA-class, BootstrapPCA-class, CA-class, MCA-class, MultivariateAnalysis, MultivariateBootstrap, MultivariateResults, MultivariateSummary, PCA-class

Examples

## Load data
data("iris")

## Compute euclidean distances
d <- dist(iris[, 1:4], method = "euclidean")

## Compute principal coordinates analysis
X <- pcoa(d)

## Screeplot
screeplot(X)

## Plot results
plot(X, extra_quali = iris$Species)

## Add convex hulls
plot(
  x = X,
  extra_quali = iris$Species,
  hull = TRUE
)

## Add tolerance ellipses
plot(
  x = X,
  extra_quali = iris$Species,
  ellipse = list(type = "tolerance", level = 0.95)
)

Benthos

Description

Abundances of Marine Species in Sea-Bed Samples

Usage

benthos

Format

A data.frame with 13 columns (sites) and 92 rows (species).

Source

http://www.carme-n.org/?sec=data7

See Also

Other datasets: colours, countries

Biplot

Description

Biplot

Usage

## S4 method for signature 'CA'
biplot(
  x,
  ...,
  axes = c(1, 2),
  type = c("symetric", "rows", "columns", "contributions"),
  active = TRUE,
  sup = TRUE,
  labels = NULL,
  col.rows = c("#E69F00", "#E69F00"),
  col.columns = c("#56B4E9", "#56B4E9"),
  pch.rows = c(16, 1),
  pch.columns = c(17, 2),
  size = c(1, 3),
  xlim = NULL,
  ylim = NULL,
  main = NULL,
  sub = NULL,
  legend = list(x = "topleft")
)

## S4 method for signature 'PCA'
biplot(
  x,
  ...,
  axes = c(1, 2),
  type = c("form", "covariance"),
  active = TRUE,
  sup = TRUE,
  labels = "variables",
  col.rows = c("#E69F00", "#E69F00"),
  col.columns = c("#56B4E9", "#56B4E9"),
  pch.rows = c(16, 1),
  lty.columns = c(1, 3),
  xlim = NULL,
  ylim = NULL,
  main = NULL,
  sub = NULL,
  legend = list(x = "topleft")
)

Arguments

x

A CA, MCA or PCA object.

...

Currently not used.

axes

A length-two numeric vector giving the dimensions to be plotted.

type

A character string specifying the biplot to be plotted (see below). It must be one of "rows", "columns", "contribution" (CA), "form" or "covariance" (PCA). Any unambiguous substring can be given.

active

A logical scalar: should the active observations be plotted?

sup

A logical scalar: should the supplementary observations be plotted?

labels

A character vector specifying whether "rows"/"individuals" and/or "columns"/"variables" names must be drawn. Any unambiguous substring can be given.

col.rows, col.columns

A length-two vector of color specification for the active and supplementary rows/columns.

pch.rows, pch.columns

A length-two vector of symbol specification for the active and supplementary rows/columns.

size

A length-two numeric vector giving range of possible sizes (greater than 0). Only used if type is "contribution" (CA).

xlim

A length-two numeric vector giving the x limits of the plot. The default value, NULL, indicates that the range of the finite values to be plotted should be used.

ylim

A length-two numeric vector giving the y limits of the plot. The default value, NULL, indicates that the range of the finite values to be plotted should be used.

main

A character string giving a main title for the plot.

sub

A character string giving a subtitle for the plot.

legend

A list of additional arguments to be passed to graphics::legend(); names of the list are used as argument names. If NULL, no legend is displayed.

lty.columns

A length-two vector of line type specification for the active and supplementary columns.

Details

A biplot is the simultaneous representation of rows and columns of a rectangular dataset. It is the generalization of a scatterplot to the case of mutlivariate data: it allows to visualize as much information as possible in a single graph (Greenacre 2010).

Biplots have the drawbacks of their advantages: they can quickly become difficult to read as they display a lot of information at once. It may then be preferable to visualize the results for individuals and variables separately.

Value

biplot() is called for its side-effects: it results in a graphic being displayed. Invisibly returns x.

PCA Biplots

form (row-metric-preserving)

The form biplot favors the representation of the individuals: the distance between the individuals approximates the Euclidean distance between rows. In the form biplot the length of a vector approximates the quality of the representation of the variable.

covariance (column-metric-preserving)

The covariance biplot favors the representation of the variables: the length of a vector approximates the standard deviation of the variable and the cosine of the angle formed by two vectors approximates the correlation between the two variables. In the covariance biplot the distance between the individuals approximates the Mahalanobis distance between rows.

CA Biplots

symetric (symetric biplot)

Represents the row and column profiles simultaneously in a common space: rows and columns are in standard coordinates. Note that the the inter-distance between any row and column items is not meaningful (i.e. the proximity between rows and columns cannot be directly interpreted).

rows (asymetric biplot)

Row principal biplot (row-metric-preserving) with rows in principal coordinates and columns in standard coordinates.

columns (asymetric biplot)

Column principal biplot (column-metric-preserving) with rows in standard coordinates and columns in principal coordinates.

contribution (asymetric biplot)

Contribution biplot with rows in principal coordinates and columns in standard coordinates multiplied by the square roots of their masses.

Author(s)

N. Frerebeau

References

Aitchison, J. and Greenacre, M. J. (2002). Biplots of Compositional Data. Journal of the Royal Statistical Society: Series C (Applied Statistics), 51(4): 375-92. doi:10.1111/1467-9876.00275.

Greenacre, M. J. (2010). Biplots in Practice. Bilbao: Fundación BBVA.

See Also

Other plot methods: plot(), screeplot(), viz_contributions(), viz_individuals(), viz_variables()

Examples

## Replicate examples from Greenacre 2007, p. 59-68
data("iris")

## Compute principal components analysis
## All rows and all columns obtain the same weight
row_w <- rep(1 / nrow(countries), nrow(countries)) # 1/13
col_w <- rep(1 / ncol(countries), ncol(countries)) # 1/6
Y <- pca(iris, scale = FALSE, sup_quali = "Species")

## Row-metric-preserving biplot (form biplot)
biplot(Y, type = "form")

## Column-metric-preserving biplot (covariance biplot)
biplot(Y, type = "covariance", legend = list(x = "bottomright"))

## Replicate examples from Greenacre 2007, p. 79-88
data("benthos")

## Compute correspondence analysis
X <- ca(benthos)

## Symetric CA biplot
biplot(X, labels = "columns", legend = list(x = "bottomright"))

## Row principal CA biplot
biplot(X, type = "row", labels = "columns", legend = list(x = "bottomright"))

## Column principal CA biplot
biplot(X, type = "column", labels = "columns", legend = list(x = "bottomright"))

## Contribution CA biplot
biplot(X, type = "contrib", labels = NULL, legend = list(x = "bottomright"))

Partial Bootstrap Analysis

Description

Checks analysis with partial bootstrap resampling.

Usage

## S4 method for signature 'CA'
bootstrap(object, n = 30)

## S4 method for signature 'PCA'
bootstrap(object, n = 30)

Arguments

object

A CA or PCA object.

n

A non-negative integer giving the number of bootstrap replications.

Value

Returns a BootstrapCA or a BootstrapPCA object.

Author(s)

N. Frerebeau

References

Greenacre, Michael J. Theory and Applications of Correspondence Analysis. London: Academic Press, 1984.

Lebart, L., Piron, M. and Morineau, A. Statistique exploratoire multidimensionnelle: visualisation et inférence en fouille de données. Paris: Dunod, 2006.

Lockyear, K. (2013). Applying Bootstrapped Correspondence Analysis to Archaeological Data. Journal of Archaeological Science, 40(12): 4744-4753. doi:10.1016/j.jas.2012.08.035.

Ringrose, T. J. (1992). Bootstrapping and Correspondence Analysis in Archaeology. Journal of Archaeological Science, 19(6): 615-629. doi:10.1016/0305-4403(92)90032-X.

Examples

## Bootstrap on CA
## Data from Lebart et al. 2006, p. 170-172
data("colours")

## Compute correspondence analysis
X <- ca(colours)

## Bootstrap (30 replicates)
Y <- bootstrap(X, n = 30)

## Not run: 
## Get replicated coordinates
get_replications(Y, margin = 1)
get_replications(Y, margin = 2)

## End(Not run)

## Plot with ellipses
viz_rows(Y)
viz_tolerance(Y, level = c(0.68, 0.95))

viz_columns(Y)
viz_tolerance(Y, level = c(0.68, 0.95))

## Plot with convex hulls
viz_columns(Y)
viz_hull(Y)

## Bootstrap on PCA
## Compute principal components analysis
data("iris")
X <- pca(iris, sup_quali = "Species")

## Bootstrap (30 replicates)
Y <- bootstrap(X, n = 30)

## Plot with ellipses
viz_variables(Y)
viz_tolerance(Y, level = c(0.68, 0.95))

Burt Table

Description

Computes the burt table of a factor table.

Usage

burt(object, ...)

## S4 method for signature 'data.frame'
burt(object, exclude = NULL, abbrev = TRUE)

Arguments

object

A data.frame.

...

Currently not used.

exclude

A vector of values to be excluded when forming the set of levels (see factor()). If NULL (the default), will make NA an extra level.

abbrev

A logical scalar: should the column names be abbreviated? If FALSE, these are of the form 'factor_level' but if abbrev = TRUE they are just 'level' which will suffice if the factors have distinct levels.

Value

A symetric matrix.

Author(s)

N. Frerebeau

See Also

Other tools: cdt()

Examples

## Create a factor table
x <- data.frame(
  A = c("a", "b", "a"),
  B = c("x", "y", "z")
)

## Complete disjunctive table
cdt(x)

## Burt table
burt(x)

Correspondence Analysis

Description

Computes a simple correspondence analysis based on the singular value decomposition.

Usage

ca(object, ...)

## S4 method for signature 'data.frame'
ca(
  object,
  rank = NULL,
  sup_row = NULL,
  sup_col = NULL,
  sup_quali = NULL,
  autodetect = FALSE
)

## S4 method for signature 'matrix'
ca(object, rank = NULL, sup_row = NULL, sup_col = NULL)

Arguments

object

A m \times p numeric matrix or a data.frame.

...

Currently not used.

rank

An integer value specifying the maximal number of components to be kept in the results. If NULL (the default), min(m, p) - 1 components will be returned.

sup_row

A vector specifying the indices of the supplementary rows.

sup_col

A vector specifying the indices of the supplementary columns.

sup_quali

A vector specifying the indices of the supplementary qualitative columns.

autodetect

A logical scalar: should non-numeric variables be automatically removed?

Value

A CA object.

Author(s)

N. Frerebeau

References

Greenacre, M. J. Theory and Applications of Correspondence Analysis. London: Academic Press, 1984.

Greenacre, M. J. Correspondence Analysis in Practice. Seconde edition. Interdisciplinary Statistics Series. Boca Raton: Chapman & Hall/CRC, 2007.

Lebart, L., Piron, M. and Morineau, A. Statistique exploratoire multidimensionnelle: visualisation et inférence en fouille de données. Paris: Dunod, 2006.

See Also

svd()

Other multivariate analysis: mca(), pca(), pcoa(), predict()

Examples

## Data from Lebart et al. 2006, p. 170-172
data("colours")

## The chi square of independence between the two variables
stats::chisq.test(colours)

## Compute correspondence analysis
X <- ca(colours)

## Plot rows
viz_rows(X, labels = TRUE)

## Plot columns
viz_columns(X, labels = TRUE)

## Get row coordinates
get_coordinates(X, margin = 1)

## Get column coordinates
get_coordinates(X, margin = 2)

## Get total inertia
sum(get_inertia(X))

## Get row contributions
get_contributions(X, margin = 1)

Complete Disjunctive Table

Description

Computes the complete disjunctive table of a factor table.

Usage

cdt(object, ...)

## S4 method for signature 'matrix'
cdt(object, exclude = NULL, abbrev = TRUE)

## S4 method for signature 'data.frame'
cdt(object, exclude = NULL, abbrev = TRUE)

Arguments

object

A data.frame.

...

Currently not used.

exclude

A vector of values to be excluded when forming the set of levels (see factor()). If NULL (the default), will make NA an extra level.

abbrev

A logical scalar: should the column names be abbreviated? If FALSE, these are of the form 'factor_level' but if abbrev = TRUE they are just 'level' which will suffice if the factors have distinct levels.

Value

A data.frame.

Author(s)

N. Frerebeau

See Also

Other tools: burt()

Examples

## Create a factor table
x <- data.frame(
  A = c("a", "b", "a"),
  B = c("x", "y", "z")
)

## Complete disjunctive table
cdt(x)

## Burt table
burt(x)

Colours

Description

Contingency table of eye and hair colours of different individuals.

Usage

colours

Format

A data.frame with 4 columns (hair colours) and 4 rows (eye colours).

Source

Lebart, L., Piron, M. and Morineau, A. Statistique exploratoire multidimensionnelle: visualisation et inférence en fouille de données. Paris: Dunod, 2006, p. 170-172

See Also

Other datasets: benthos, countries

Countries

Description

Student ratings of 13 countries on six attributes.

Usage

countries

Format

A data.frame with 6 columns (attributes) and 13 rows (countries).

Source

Greenacre, M. J. Biplots in Practice. Bilbao: Fundación BBVA, 2010.

See Also

Other datasets: benthos, colours

Object Description

Description

Object Description

Usage

## S4 method for signature 'CA'
describe(x, ...)

## S4 method for signature 'PCA'
describe(x, ...)

Arguments

x

A CA, MCA or PCA object.

...

Further parameters to be passed to cat().

Value

describe() is called for its side-effects. Invisibly returns x.

Author(s)

N. Frerebeau

See Also

Other summary: summary(), tidy()

Examples

## Data from Lebart et al. 2006, p. 170-172
data("colours")

## Compute correspondence analysis
X <- ca(colours)

## Rows summary
summary(X, margin = 1)

## Columns summary
summary(X, margin = 2)

Defunct Functions in dimensio

Description

These functions are defunct and have been replaced.

Usage

plot_rows(...)

plot_individuals(...)

plot_columns(...)

plot_variables(...)

plot_variance(...)

plot_contributions(...)

plot_cos2(...)

Arguments

...

Not used.

Deprecated Functions in dimensio

Description

These functions still work but will be removed (defunct) in the next version.

Dimnames of an Object

Description

Retrieve or set the dimnames of an object.

Usage

## S4 method for signature 'MultivariateAnalysis'
dim(x)

## S4 method for signature 'MultivariateAnalysis'
rownames(x, do.NULL = TRUE, prefix = "row")

## S4 method for signature 'MultivariateAnalysis'
colnames(x, do.NULL = TRUE, prefix = "col")

## S4 method for signature 'MultivariateAnalysis'
dimnames(x)

Arguments

x

An object from which to retrieve the row or column names (a CA or PCA object).

do.NULL

A logical scalar. If FALSE and names are NULL, names are created.

prefix

A character string specifying the prefix for created names.

Author(s)

N. Frerebeau

See Also

Other mutators: subset()

Export Results

Description

Creates a Zip archive of all results in CSV format.

Usage

export(object, ...)

## S4 method for signature 'MultivariateAnalysis'
export(object, file, flags = "-r9Xj", ...)

## S4 method for signature 'PCOA'
export(object, file, flags = "-r9Xj", ...)

Arguments

object

A CA, MCA or PCA object.

...

Currently not used.

file

A character string specifying the pathname of the zip file.

flags

A character string of flags (see utils::zip()).

Author(s)

N. Frerebeau

See Also

utils::write.csv(), utils::zip()

Other getters: get_contributions(), get_coordinates(), get_data(), get_eigenvalues()

Examples

## Not run: 
## Load data
data("iris")

## Compute principal components analysis
X <- pca(iris, sup_quali = "Species")

## Export results
export(X, file = "results.zip")

## End(Not run)

Get Contributions

Description

Get Contributions

Usage

get_contributions(x, ...)

get_correlations(x, ...)

get_cos2(x, ...)

## S4 method for signature 'MultivariateAnalysis'
get_contributions(x, margin = 1)

## S4 method for signature 'PCA'
get_correlations(x, sup_name = ".sup")

## S4 method for signature 'MultivariateAnalysis'
get_cos2(x, margin = 1, sup_name = ".sup")

Arguments

x

An object from which to get element(s) (a CA, MCA or PCA object).

...

Currently not used.

margin

A length-one numeric vector giving the subscript which the data will be returned: 1 indicates individuals/rows (the default), 2 indicates variables/columns.

sup_name

A character string specifying the name of the column to create for supplementary points attribution (see below).

Value

Author(s)

N. Frerebeau

See Also

Other getters: export(), get_coordinates(), get_data(), get_eigenvalues()

Get Coordinates

Description

Get Coordinates

Usage

get_coordinates(x, ...)

get_replications(x, ...)

## S4 method for signature 'MultivariateAnalysis'
get_coordinates(x, margin = 1, principal = TRUE, sup_name = ".sup")

## S4 method for signature 'PCOA'
get_coordinates(x)

## S4 method for signature 'MultivariateBootstrap'
get_replications(x, margin = 1)

## S4 method for signature 'BootstrapPCA'
get_replications(x)

Arguments

x

An object from which to get element(s) (a CA, MCA or PCA object).

...

Currently not used.

margin

A length-one numeric vector giving the subscript which the data will be returned: 1 indicates individuals/rows (the default), 2 indicates variables/columns.

principal

A logical scalar: should principal coordinates be returned? If FALSE, standard coordinates are returned.

sup_name

A character string specifying the name of the column to create for supplementary points attribution (see below).

Value

Author(s)

N. Frerebeau

See Also

Other getters: export(), get_contributions(), get_data(), get_eigenvalues()

Examples

## Load data
data("iris")

## Compute principal components analysis
X <- pca(iris, scale = TRUE, sup_row = 5:10, sup_quali = "Species")

## Get row principal coordinates
head(get_coordinates(X, margin = 1, principal = TRUE))

## Get row standard coordinates
head(get_coordinates(X, margin = 1, principal = FALSE))

## Tidy principal coordinates
head(tidy(X, margin = 1))
head(tidy(X, margin = 2))

head(augment(X, margin = 1, axes = c(1, 2)))
head(augment(X, margin = 2, axes = c(1, 2)))

Get Original Data

Description

Get Original Data

Usage

get_data(x, ...)

## S4 method for signature 'MultivariateAnalysis'
get_data(x)

Arguments

x

An object from which to get element(s) (a CA, MCA or PCA object).

...

Currently not used.

Value

Returns a data.frame of original data.

Author(s)

N. Frerebeau

See Also

Other getters: export(), get_contributions(), get_coordinates(), get_eigenvalues()

Get Eigenvalues

Description

Get Eigenvalues

Usage

get_eigenvalues(x)

get_variance(x, ...)

get_distances(x, ...)

get_inertia(x, ...)

## S4 method for signature 'MultivariateAnalysis'
get_distances(x, margin = 1)

## S4 method for signature 'MultivariateAnalysis'
get_eigenvalues(x)

## S4 method for signature 'PCOA'
get_eigenvalues(x)

## S4 method for signature 'MultivariateAnalysis'
get_inertia(x, margin = 1)

## S4 method for signature 'MultivariateAnalysis'
get_variance(x, digits = 2)

Arguments

x

An object from which to get element(s) (a CA, MCA or PCA object).

...

Currently not used.

margin

A length-one numeric vector giving the subscript which the data will be returned: 1 indicates individuals/rows (the default), 2 indicates variables/columns.

digits

An integer indicating the number of decimal places to be used.

Value

Author(s)

N. Frerebeau

See Also

Other getters: export(), get_contributions(), get_coordinates(), get_data()

Non-Overlapping Text Labels

Description

Optimize the location of text labels to minimize overplotting text.

Usage

label(
  x,
  y = NULL,
  labels = seq_along(x$x),
  type = c("text", "shadow", "box"),
  ...
)

Arguments

x, y

A numeric vector giving the x and y coordinates of a set of points. If y is NULL, an attempt is made to interpret x in a suitable way (see grDevices::xy.coords()).

labels

A character vector or expression specifying the text to be written.

type

A character string specifying the shape of the field. It must be one of "text", "shadow" or "box". Any unambiguous substring can be given.

...

Further arguments to be passed to graphics::text(), particularly, character expansion, cex and color, col.

Value

label() is called it for its side-effects: it results in a graphic being displayed.

Author(s)

N. Frerebeau

Source

This function is modeled after car::pointLabel() (originally from the maptools package).

See Also

graphics::text()

Multiple Correspondence Analysis

Description

Computes a multiple correspondence analysis.

Usage

mca(object, ...)

## S4 method for signature 'data.frame'
mca(
  object,
  rank = NULL,
  sup_row = NULL,
  sup_col = NULL,
  sup_quanti = NULL,
  autodetect = FALSE
)

## S4 method for signature 'matrix'
mca(object, rank = NULL, sup_row = NULL, sup_col = NULL)

Arguments

object

A m \times p numeric matrix or a data.frame.

...

Currently not used.

rank

An integer value specifying the maximal number of components to be kept in the results. If NULL (the default), min(m, p) - 1 components will be returned.

sup_row

A vector specifying the indices of the supplementary rows.

sup_col

A vector specifying the indices of the supplementary categorical columns.

sup_quanti

A vector specifying the indices of the supplementary quantitative columns.

autodetect

A logical scalar: should numeric variables be automatically removed (except sup_quanti)?

Value

A MCA object.

Author(s)

N. Frerebeau

References

Lebart, L., Piron, M. and Morineau, A. Statistique exploratoire multidimensionnelle: visualisation et inférence en fouille de données. Paris: Dunod, 2006.

See Also

svd(), cdt()

Other multivariate analysis: ca(), pca(), pcoa(), predict()

Principal Components Analysis

Description

Computes a principal components analysis based on the singular value decomposition.

Usage

pca(object, ...)

## S4 method for signature 'data.frame'
pca(
  object,
  center = TRUE,
  scale = TRUE,
  rank = NULL,
  sup_row = NULL,
  sup_col = NULL,
  sup_quali = NULL,
  weight_row = NULL,
  weight_col = NULL,
  autodetect = FALSE
)

## S4 method for signature 'matrix'
pca(
  object,
  center = TRUE,
  scale = TRUE,
  rank = NULL,
  sup_row = NULL,
  sup_col = NULL,
  weight_row = NULL,
  weight_col = NULL
)

Arguments

object

A m \times p numeric matrix or a data.frame.

...

Currently not used.

center

A logical scalar: should the variables be shifted to be zero centered?

scale

A logical scalar: should the variables be scaled to unit variance?

rank

An integer value specifying the maximal number of components to be kept in the results. If NULL (the default), p - 1 components will be returned.

sup_row

A vector specifying the indices of the supplementary rows.

sup_col

A vector specifying the indices of the supplementary columns.

sup_quali

A vector specifying the indices of the supplementary qualitative columns.

weight_row

A numeric vector specifying the active row (individual) weights. If NULL (the default), uniform weights are used. Row weights are internally normalized to sum 1

weight_col

A numeric vector specifying the active column (variable) weights. If NULL (the default), uniform weights (1) are used.

autodetect

A logical scalar: should non-numeric variables be automatically removed (except sup_quali)?

Value

A PCA object.

Author(s)

N. Frerebeau

References

Lebart, L., Piron, M. and Morineau, A. Statistique exploratoire multidimensionnelle: visualisation et inférence en fouille de données. Paris: Dunod, 2006.

See Also

svd()

Other multivariate analysis: ca(), mca(), pcoa(), predict()

Examples

## Load data
data("iris")

## Compute principal components analysis
X <- pca(iris, sup_quali = "Species")

## Get eigenvalues
get_eigenvalues(X)

## Get individual cos2
head(get_cos2(X, margin = 1))

## Get variable contributions
get_contributions(X, margin = 2)

## Get correlations between variables and dimensions
get_correlations(X)

Principal Coordinates Analysis

Description

Computes classical (metric) multidimensional scaling.

Usage

pcoa(object, ...)

## S4 method for signature 'dist'
pcoa(object, rank = 2)

Arguments

object

A distance structure.

...

Currently not used.

rank

An integer value specifying the maximal number dimension of the space which the data are to be represented in.

Value

A PCOA object.

Author(s)

N. Frerebeau

References

Gower, J. C. (1966). Some Distance Properties of Latent Root and Vector Methods Used in Multivariate Analysis. Biometrika, 53(3‑4): 325-338. doi:10.1093/biomet/53.3-4.325.

See Also

stats::cmdscale()

Other multivariate analysis: ca(), mca(), pca(), predict()

Examples

## Load data
data("iris")

## Compute euclidean distances
d <- dist(iris[, 1:4], method = "euclidean")

## Compute principal coordinates analysis
X <- pcoa(d)

## Screeplot
screeplot(X)

## Plot results
plot(X, extra_quali = iris$Species)

## Add convex hulls
plot(
  x = X,
  extra_quali = iris$Species,
  hull = TRUE
)

## Add tolerance ellipses
plot(
  x = X,
  extra_quali = iris$Species,
  ellipse = list(type = "tolerance", level = 0.95)
)

Plot Coordinates

Description

Plot Coordinates

Usage

## S4 method for signature 'PCOA,missing'
plot(
  x,
  ...,
  axes = c(1, 2),
  labels = FALSE,
  extra_quali = NULL,
  extra_quanti = NULL,
  ellipse = NULL,
  hull = FALSE,
  color = NULL,
  fill = FALSE,
  symbol = FALSE,
  size = c(1, 6),
  xlim = NULL,
  ylim = NULL,
  main = NULL,
  sub = NULL,
  ann = graphics::par("ann"),
  frame.plot = TRUE,
  panel.first = NULL,
  panel.last = NULL,
  legend = list(x = "topleft")
)

Arguments

x

An R object.

...

Further graphical parameters.

axes

A length-two numeric vector giving the dimensions to be plotted.

labels

A logical scalar: should labels be drawn? Labeling a large number of points can be computationally expensive and make the graph difficult to read. A selection of points to label can be provided using a list of two named elements, filter (a string specifying how to filter the labels to be drawn) and n (an integer specifying the number of labels to be drawn). See examples below.

extra_quali

An optional vector of qualitative data for aesthetics mapping.

extra_quanti

An optional vector of quantitative data for aesthetics mapping. If a single character string is passed, it must be one of "observation", "mass", "sum", "contribution" or "cos2" (see augment()).

ellipse

A list of additional arguments to be passed to viz_ellipses(); names of the list are used as argument names. If NULL, no ellipse are displayed.

hull

A logical scalar: should convex hulls be displayed?

color

The colors for lines and points (will be mapped to extra_quanti or extra_quali; if both are set, the latter has priority). Ignored if set to FALSE. If NULL, the default color scheme will be used.

fill

The background colors for points (will be mapped to extra_quanti or extra_quali; if both are set, the latter has priority). Ignored if set to FALSE.

symbol

A vector of plotting characters or symbols (will be mapped to extra_quali). This can either be a single character or an integer code for one of a set of graphics symbols. If symbol is a named a named vector, then the symbols will be associated with their name within extra_quali. Ignored if set to FALSE.

size

A length-two numeric vector giving range of possible sizes (greater than 0; will be mapped to extra_quanti). Ignored if set to FALSE.

xlim

A length-two numeric vector giving the x limits of the plot. The default value, NULL, indicates that the range of the finite values to be plotted should be used.

ylim

A length-two numeric vector giving the y limits of the plot. The default value, NULL, indicates that the range of the finite values to be plotted should be used.

main

A character string giving a main title for the plot.

sub

A character string giving a subtitle for the plot.

ann

A logical scalar: should the default annotation (title and x and y axis labels) appear on the plot?

frame.plot

A logical scalar: should a box be drawn around the plot?

panel.first

An expression to be evaluated after the plot axes are set up but before any plotting takes place. This can be useful for drawing background grids.

panel.last

An expression to be evaluated after plotting has taken place but before the axes, title and box are added.

legend

A list of additional arguments to be passed to graphics::legend(); names of the list are used as argument names. If NULL, no legend is displayed.

Author(s)

N. Frerebeau

See Also

Other plot methods: biplot(), screeplot(), viz_contributions(), viz_individuals(), viz_variables()

Predict New Coordinates

Description

Predict the projection of new individuals/rows or variables/columns.

Usage

## S4 method for signature 'CA'
predict(object, newdata, margin = 1)

## S4 method for signature 'MCA'
predict(object, newdata, margin = 1)

## S4 method for signature 'PCA'
predict(object, newdata, margin = 1)

Arguments

object

A CA or PCA object.

newdata

An object of supplementary points coercible to a matrix for which to compute principal coordinates.

margin

A length-one numeric vector giving the subscript which the data will be predicted: 1 indicates individuals/rows (the default), 2 indicates variables/columns.

Value

A data.frame of coordinates.

Author(s)

N. Frerebeau

See Also

Other multivariate analysis: ca(), mca(), pca(), pcoa()

Examples

## Create a matrix
A <- matrix(data = sample(1:10, 100, TRUE), nrow = 10, ncol = 10)

## Compute correspondence analysis
X <- ca(A, sup_row = 8:10, sup_col = 7:10)

## Predict new row coordinates
Y <- matrix(data = sample(1:10, 120, TRUE), nrow = 20, ncol = 6)
predict(X, Y, margin = 1)

## Predict new column coordinates
Z <- matrix(data = sample(1:10, 140, TRUE), nrow = 7, ncol = 20)
predict(X, Z, margin = 2)

Build a Legend

Description

Build a Legend

Usage

prepare_legend(x, args, points = TRUE, lines = TRUE)

Arguments

x

A data.frame returned by prepare_plot().

args

A list of additional arguments to be passed to graphics::legend(); names of the list are used as argument names. If NULL or empty, no legend is displayed.

points

A logical scalar: legend for points?

lines

A logical scalar: legend for lines?

Author(s)

N. Frerebeau

Prepare Data for Plotting

Description

Prepare Data for Plotting

Usage

prepare_plot(
  x,
  margin,
  ...,
  axes = c(1, 2),
  active = TRUE,
  sup = TRUE,
  principal = TRUE,
  extra_quali = NULL,
  extra_quanti = NULL,
  color = NULL,
  fill = FALSE,
  symbol = NULL,
  size = c(1, 6),
  line_type = NULL,
  line_width = size
)

Arguments

x

A MultivariateAnalysis object.

margin

A length-one numeric vector giving the subscript which the data will be returned: 1 indicates individuals/rows (the default), 2 indicates variables/columns.

...

Further graphical parameters.

axes

A length-two numeric vector giving the dimensions to be plotted.

active

A logical scalar: should the active observations be plotted?

sup

A logical scalar: should the supplementary observations be plotted?

principal

A logical scalar: should principal coordinates be returned? If FALSE, standard coordinates are returned.

extra_quali

An optional vector of qualitative data for aesthetics mapping.

extra_quanti

An optional vector of quantitative data for aesthetics mapping. If a single character string is passed, it must be one of "observation", "mass", "sum", "contribution" or "cos2" (see augment()).

color

The colors for lines and points (will be mapped to extra_quanti or extra_quali; if both are set, the latter has priority). Ignored if set to FALSE. If NULL, the default color scheme will be used.

fill

The background colors for points (will be mapped to extra_quanti or extra_quali; if both are set, the latter has priority). Ignored if set to FALSE.

symbol

A vector of plotting characters or symbols (will be mapped to extra_quali). This can either be a single character or an integer code for one of a set of graphics symbols. If symbol is a named a named vector, then the symbols will be associated with their name within extra_quali. Ignored if set to FALSE.

size

A length-two numeric vector giving range of possible sizes (greater than 0; will be mapped to extra_quanti). Ignored if set to FALSE.

line_type

A specification for the line type (will be mapped to extra_quali). If line_type is a named a named vector, then the line types will be associated with their name within extra_quali. Ignored if set to FALSE.

line_width

A specification for the line type and width (will be mapped to extra_quanti). Ignored if set to FALSE.

Value

A data.frame with the following columns:

x

Coordinates along x.

y

Coordinates along y.

extra_quali

Extra qualitative variable to be highlighted.

extra_quanti

Extra quantitative variable to be highlighted.

label

Label.

sup

Is supplementary?

col

Color for lines and symbols.

bg

Background color for symbols.

pch

Symbols.

cex

Symbol sizes.

lty

Line types.

lwd

Line widths.

Author(s)

N. Frerebeau

Objects exported from other packages

Description

These objects are imported from other packages. Follow the links below to see their documentation.

khroma

color, palette_color_continuous, palette_color_discrete, palette_color_picker, palette_line, palette_shape, palette_size_sequential

Scree Plot

Description

Plot eigenvalues (scree plot) or variances histogram.

Usage

## S4 method for signature 'MultivariateAnalysis'
screeplot(
  x,
  ...,
  eigenvalues = FALSE,
  cumulative = FALSE,
  labels = TRUE,
  limit = 10,
  col = "grey90",
  border = "grey10",
  col.cumulative = "red",
  lty.cumulative = "solid",
  lwd.cumulative = 2
)

## S4 method for signature 'PCOA'
screeplot(
  x,
  ...,
  labels = FALSE,
  limit = NULL,
  col = "grey90",
  border = "grey10"
)

Arguments

x

A CA, MCA or PCA object.

...

Extra parameters to be passed to graphics::barplot().

eigenvalues

A logical scalar: should the eigenvalues be plotted instead of variance/inertia?

cumulative

A logical scalar: should the cumulative percentages of variance be plotted?

labels

A logical scalar: should text labels be drawn on top of bars?

limit

An integer specifying the number of top elements to be displayed.

col, border

A character string specifying the bars infilling and border colors.

col.cumulative

A specification for the line color.

lty.cumulative

A specification for the line type.

lwd.cumulative

A specification for the line width.

Value

screeplot() is called for its side-effects: it results in a graphic being displayed. Invisibly returns x.

Author(s)

N. Frerebeau

See Also

Other plot methods: biplot(), plot(), viz_contributions(), viz_individuals(), viz_variables()

Examples

## Load data
data("iris")

## Compute principal components analysis
X <- pca(iris, scale = TRUE, sup_quali = "Species")

## Screeplot
screeplot(X)
screeplot(X, cumulative = TRUE)

Extract Parts of an Object

Description

Operators acting on objects to extract parts.

Usage

## S4 method for signature 'CA,ANY,missing'
x[[i]]

## S4 method for signature 'PCA,ANY,missing'
x[[i]]

Arguments

x

An object from which to extract element(s) or in which to replace element(s).

i

A character string specifying elements to extract. Any unambiguous substring can be given (see details).

Details

If i is "data", returns a list with the following elements:

data

A numeric matrix of raw data.

mean

A numeric vector giving the variables means (PCA).

sd

A numeric vector giving the variables standard deviations (PCA).

If i is "rows", returns a list with the following elements:

coord

A numeric matrix of rows/individuals coordinates.

cos2

A numeric matrix of rows/individuals squared cosine.

masses

A numeric vector giving the rows masses/individual weights.

sup

A logical vector specifying whether a point is a supplementary observation or not.

If i is "columns", returns a list with the following elements:

coord

A numeric matrix of columns/variables coordinates.

cor

A numeric matrix of correlation between variables and the dimensions (PCA).

cos2

A numeric matrix of columns/variables squared cosine.

masses

A numeric vector giving the columns masses/variable weights.

sup

A logical vector specifying whether a point is a supplementary observation or not.

If i is "eigenvalues", returns a numeric vector of eigenvalues.

Value

A list.

Author(s)

N. Frerebeau

See Also

Other mutators: dimnames()

Examples

## Load data
data("iris")

## Compute principal components analysis
X <- pca(iris, scale = TRUE, sup_row = 8:10, sup_col = 1, sup_quali = "Species")

## Get results for the individuals
X[["rows"]]

Object Summaries

Description

Provides a summary of the results of a multivariate data analysis.

Usage

## S4 method for signature 'MultivariateSummary'
as.data.frame(x, row.names = NULL, optional = FALSE, ...)

## S4 method for signature 'CA'
summary(
  object,
  ...,
  axes = c(1, 2),
  margin = 1,
  active = TRUE,
  sup = TRUE,
  rank = NULL
)

## S4 method for signature 'PCA'
summary(
  object,
  ...,
  axes = c(1, 2),
  margin = 1,
  active = TRUE,
  sup = TRUE,
  rank = NULL
)

Arguments

x

A MultivariateSummary object.

row.names

A character vector giving the row names for the data frame, or NULL.

optional

A logical scalar: should the names of the variables in the data frame be checked? If FALSE then the names of the variables in the data frame are checked to ensure that they are syntactically valid variable names and are not duplicated.

...

Currently not used.

object

A CA, MCA or PCA object.

axes

A length-two numeric vector giving the dimensions to be summarized.

margin

A length-one numeric vector giving the subscript which the data will be summarized: 1 indicates individuals/rows (the default), 2 indicates variables/columns.

active

A logical scalar: should the active observations be summarized?

sup

A logical scalar: should the supplementary observations be summarized?

rank

An integer value specifying the maximal number of components to be kept in the results. Deprecated, use axes instead.

Author(s)

N. Frerebeau

See Also

Other summary: describe(), tidy()

Examples

## Data from Lebart et al. 2006, p. 170-172
data("colours")

## Compute correspondence analysis
X <- ca(colours)

## Rows summary
summary(X, margin = 1)

## Columns summary
summary(X, margin = 2)

Singular Value Decomposition of a Matrix

Description

Singular Value Decomposition of a Matrix

Usage

svd2(x, rank = Inf)

Arguments

x

A m \times p numeric matrix.

rank

An integer value specifying the maximal number of components to be kept in the results.

Value

A list with the following elements:

d

A vector containing the singular values of x, of length rank, sorted decreasingly.

u

A matrix whose columns contain the left singular vectors of x. Dimension c(m, rank).

v

A matrix whose columns contain the right singular vectors of x. Dimension c(p, rank).

Note

In both PCA and PCA-cor whitening there is a sign-ambiguity in the eigenvector matrices. In order to resolve the sign-ambiguity we use eigenvector matrices with a positive diagonal. This has the effect to make cross-correlations and cross-correlations positive diagonal for PCA.

Tidy Coordinates

Description

Tidy Coordinates

Usage

tidy(x, ...)

augment(x, ...)

## S4 method for signature 'MultivariateAnalysis'
augment(x, ..., margin = 1, axes = c(1, 2), principal = TRUE)

## S4 method for signature 'MultivariateAnalysis'
tidy(x, ..., margin = 1, principal = TRUE)

Arguments

x

A CA, MCA or PCA object.

...

Currently not used.

margin

A length-one numeric vector giving the subscript which the data will be returned: 1 indicates individuals/rows (the default), 2 indicates variables/columns.

axes

A length-two numeric vector giving the dimensions for which to compute results.

principal

A logical scalar: should principal coordinates be returned? If FALSE, standard coordinates are returned.

Value

tidy() returns a long data.frame with the following columns:

label

Row/column names of the original data.

component

Component.

supplementary

Whether an observation is active or supplementary.

coordinate

Coordinates.

contribution

Contributions to the definition of the components.

cos2

cos^2.

augment() returns a wide data.frame of the row/column coordinates along axes and the following columns:

label

Row/column names of the original data.

supplementary

Whether an observation is active or supplementary.

mass

Weight/mass of each observation.

sum

Sum of squared coordinates along axes.

contribution

Joint contributions to the definition of axes.

cos2

Joint cos^2 along axes.

Author(s)

N. Frerebeau

See Also

Other summary: describe(), summary()

Examples

## Load data
data("iris")

## Compute principal components analysis
X <- pca(iris, scale = TRUE, sup_row = 5:10, sup_quali = "Species")

## Get row principal coordinates
head(get_coordinates(X, margin = 1, principal = TRUE))

## Get row standard coordinates
head(get_coordinates(X, margin = 1, principal = FALSE))

## Tidy principal coordinates
head(tidy(X, margin = 1))
head(tidy(X, margin = 2))

head(augment(X, margin = 1, axes = c(1, 2)))
head(augment(X, margin = 2, axes = c(1, 2)))

Confidence Ellipses

Description

Plots confidence ellipses.

Usage

viz_confidence(x, y, ...)

wrap_confidence(x, y, ...)

## S4 method for signature 'numeric,numeric'
viz_confidence(
  x,
  y,
  ...,
  group = NULL,
  level = 0.95,
  color = NULL,
  fill = FALSE,
  symbol = FALSE
)

## S4 method for signature 'MultivariateAnalysis,missing'
viz_confidence(
  x,
  ...,
  group = NULL,
  level = 0.95,
  color = NULL,
  fill = FALSE,
  symbol = FALSE
)

## S4 method for signature 'MultivariateBootstrap,missing'
viz_confidence(
  x,
  ...,
  level = 0.95,
  color = FALSE,
  fill = FALSE,
  symbol = FALSE
)

## S4 method for signature 'PCOA,missing'
viz_confidence(
  x,
  ...,
  axes = c(1, 2),
  group = NULL,
  level = 0.95,
  color = NULL,
  fill = FALSE,
  symbol = FALSE
)

## S4 method for signature 'numeric,numeric'
wrap_confidence(x, y, group = NULL, level = 0.95)

## S4 method for signature 'MultivariateAnalysis,missing'
wrap_confidence(
  x,
  margin = 1,
  axes = c(1, 2),
  group = NULL,
  level = 0.95,
  principal = TRUE
)

## S4 method for signature 'PCOA,missing'
wrap_confidence(x, axes = c(1, 2), group = NULL, level = 0.95)

Arguments

x, y

A numeric vector. If y is missing, x must be an object from which to wrap observations (a CA, MCA or PCA object).

...

Further graphical parameters to be passed to graphics::polygon().

group

A vector specifying the group an observation belongs to.

level

A numeric vector specifying the confidence/tolerance level.

color

The colors for borders (will be mapped to group). Ignored if set to FALSE. If NULL, the default color scheme will be used.

fill

The background colors (will be mapped to group). Ignored if set to FALSE.

symbol

A vector of symbols (will be mapped to group). Ignored if set to FALSE.

axes

A length-two numeric vector giving the dimensions for which to compute results.

margin

A length-one numeric vector giving the subscript which the data will be returned: 1 indicates individuals/rows (the default), 2 indicates variables/columns.

principal

A logical scalar: should principal coordinates be used? If FALSE, standard coordinates are returned.

Value

wrap_confidence() returns a data.frame of envelope x and y coordinates. An extra column named group is added specifying the group an observation belongs to.

viz_confidence()is called for its side-effects: it results in a graphic being displayed. Invisibly returns x.

Author(s)

N. Frerebeau

See Also

Other envelopes: viz_ellipses(), viz_hull(), viz_tolerance()

Examples

## Load data
data("iris")

## Compute principal components analysis
X <- pca(iris, scale = TRUE, sup_quali = "Species")

## Compute confidence ellipse coordinates
conf <- wrap_confidence(X, margin = 1, group = "Species", level = 0.95)

## Plot confidence ellipses
col <- c("#004488", "#DDAA33", "#BB5566")
viz_rows(X, extra_quali = iris$Species, color = col)
viz_confidence(X, group = iris$Species, color = col, level = 0.95)

Visualize Contributions and cos2

Description

Plots contributions histogram and cos^2 scatterplot.

Usage

viz_contributions(x, ...)

viz_cos2(x, ...)

## S4 method for signature 'MultivariateAnalysis'
viz_contributions(
  x,
  ...,
  margin = 2,
  axes = 1,
  sort = TRUE,
  decreasing = TRUE,
  limit = 10,
  horiz = FALSE,
  col = "grey90",
  border = "grey10"
)

## S4 method for signature 'MultivariateAnalysis'
viz_cos2(
  x,
  ...,
  margin = 2,
  axes = 1,
  active = TRUE,
  sup = TRUE,
  sort = TRUE,
  decreasing = TRUE,
  limit = 10,
  horiz = FALSE,
  col = "grey90",
  border = "grey10"
)

Arguments

x

A CA, MCA or PCA object.

...

Extra parameters to be passed to graphics::barplot().

margin

A length-one numeric vector giving the subscript which the data will be returned: 1 indicates individuals/rows (the default), 2 indicates variables/columns.

axes

A numeric vector giving the dimensions to be plotted.

sort

A logical scalar: should the data be sorted?

decreasing

A logical scalar: should the sort order be decreasing? Only used if sort is TRUE.

limit

An integer specifying the number of top elements to be displayed.

horiz

A logical scalar: should the bars be drawn horizontally with the first at the bottom?

col, border

A character string specifying the bars infilling and border colors.

active

A logical scalar: should the active observations be plotted?

sup

A logical scalar: should the supplementary observations be plotted?

Details

The red dashed line indicates the expected average contribution (variables with a contribution larger than this cutoff can be considered as important in contributing to the component).

Value

viz_contributions() and viz_cos2() are called for their side-effects: they result in a graphic being displayed. Invisibly return x.

Author(s)

N. Frerebeau

See Also

Other plot methods: biplot(), plot(), screeplot(), viz_individuals(), viz_variables()

Examples

## Load data
data("iris")

## Compute principal components analysis
X <- pca(iris, scale = TRUE, sup_quali = "Species")

## Get row contributions
head(get_contributions(X, margin = 1))

## Plot contributions
viz_contributions(X, axes = 1)

## Plot cos2
viz_cos2(X, axes = 1)

Ellipses

Description

Plots ellipses.

Usage

viz_ellipses(x, y, ...)

## S4 method for signature 'numeric,numeric'
viz_ellipses(
  x,
  y,
  ...,
  group = NULL,
  type = c("tolerance", "confidence"),
  level = 0.95,
  color = NULL,
  fill = FALSE,
  symbol = FALSE
)

## S4 method for signature 'MultivariateAnalysis,missing'
viz_ellipses(
  x,
  ...,
  group = NULL,
  type = c("tolerance", "confidence"),
  level = 0.95,
  color = NULL,
  fill = FALSE,
  symbol = FALSE
)

## S4 method for signature 'PCOA,missing'
viz_ellipses(
  x,
  ...,
  group = NULL,
  type = c("tolerance", "confidence"),
  level = 0.95,
  color = NULL,
  fill = FALSE,
  symbol = FALSE
)

Arguments

x, y

A numeric vector. If y is missing, x must be an object from which to wrap observations (a CA, MCA or PCA object).

...

Further graphical parameters to be passed to graphics::polygon().

group

A vector specifying the group an observation belongs to.

type

A character string specifying the ellipse to draw. It must be one of "tolerance" or "confidence"). Any unambiguous substring can be given.

level

A numeric vector specifying the confidence/tolerance level.

color

The colors for borders (will be mapped to group). Ignored if set to FALSE. If NULL, the default color scheme will be used.

fill

The background colors (will be mapped to group). Ignored if set to FALSE.

symbol

A vector of symbols (will be mapped to group). Ignored if set to FALSE.

Value

viz_ellipses()is called for its side-effects: it results in a graphic being displayed. Invisibly returns x.

Author(s)

N. Frerebeau

See Also

Other envelopes: viz_confidence(), viz_hull(), viz_tolerance()

Examples

## Load data
data("iris")

## Compute principal components analysis
X <- pca(iris, scale = TRUE, sup_quali = "Species")

## Plot with tolerance ellipses
col <- c("#004488", "#DDAA33", "#BB5566")
viz_rows(X, extra_quali = iris$Species, color = col)
viz_ellipses(
  x = X,
  type = "tolerance",
  level = c(0.68, 0.95),
  group = iris$Species,
  color = col
)

Convex Hulls

Description

Plots convex hull of a set of observations.

Usage

viz_hull(x, y, ...)

wrap_hull(x, y, ...)

## S4 method for signature 'numeric,numeric'
viz_hull(x, y, ..., group = NULL, color = NULL, fill = FALSE, symbol = FALSE)

## S4 method for signature 'MultivariateAnalysis,missing'
viz_hull(x, ..., group = NULL, color = NULL, fill = FALSE, symbol = FALSE)

## S4 method for signature 'MultivariateBootstrap,missing'
viz_hull(x, ..., color = FALSE, fill = FALSE, symbol = FALSE)

## S4 method for signature 'PCOA,missing'
viz_hull(x, ..., group = NULL, color = FALSE, fill = FALSE, symbol = FALSE)

## S4 method for signature 'numeric,numeric'
wrap_hull(x, y, group = NULL)

## S4 method for signature 'MultivariateAnalysis,missing'
wrap_hull(x, margin = 1, axes = c(1, 2), group = NULL, principal = TRUE)

## S4 method for signature 'PCOA,missing'
wrap_hull(x, axes = c(1, 2), group = NULL)

Arguments

x, y

A numeric vector. If y is missing, x must be an object from which to wrap observations (a CA, MCA or PCA object).

...

Further graphical parameters to be passed to graphics::polygon().

group

A vector specifying the group an observation belongs to.

color

The colors for borders (will be mapped to group). Ignored if set to FALSE. If NULL, the default color scheme will be used.

fill

The background colors (will be mapped to group). Ignored if set to FALSE.

symbol

A vector of symbols (will be mapped to group). Ignored if set to FALSE.

margin

A length-one numeric vector giving the subscript which the data will be returned: 1 indicates individuals/rows (the default), 2 indicates variables/columns.

axes

A length-two numeric vector giving the dimensions for which to compute results.

principal

A logical scalar: should principal coordinates be used? If FALSE, standard coordinates are returned.

Value

wrap_hull() returns a data.frame of envelope x and y coordinates. An extra column named group is added specifying the group an observation belongs to.

viz_hull()is called for its side-effects: it results in a graphic being displayed. Invisibly returns x.

Author(s)

N. Frerebeau

See Also

Other envelopes: viz_confidence(), viz_ellipses(), viz_tolerance()

Examples

## Load data
data("iris")

## Compute principal components analysis
X <- pca(iris, scale = TRUE, sup_quali = "Species")

## Compute convex hull coordinates
hulls <- wrap_hull(X, margin = 1, group = "Species")

## Plot convex hulls
col <- c("#004488", "#DDAA33", "#BB5566")
viz_rows(X, extra_quali = iris$Species, color = col)
viz_hull(X, group = iris$Species, color = col)

Visualize Individuals Factor Map

Description

Plots row/individual principal coordinates.

Usage

viz_individuals(x, ...)

viz_rows(x, ...)

## S4 method for signature 'MultivariateAnalysis'
viz_rows(
  x,
  ...,
  axes = c(1, 2),
  active = TRUE,
  sup = TRUE,
  labels = FALSE,
  extra_quali = NULL,
  extra_quanti = NULL,
  ellipse = NULL,
  hull = NULL,
  color = NULL,
  fill = FALSE,
  symbol = FALSE,
  size = c(1, 6),
  xlim = NULL,
  ylim = NULL,
  main = NULL,
  sub = NULL,
  panel.first = NULL,
  panel.last = NULL,
  legend = list(x = "topleft")
)

## S4 method for signature 'BootstrapCA'
viz_rows(
  x,
  ...,
  axes = c(1, 2),
  color = FALSE,
  fill = FALSE,
  symbol = FALSE,
  legend = NULL
)

## S4 method for signature 'PCA'
viz_individuals(
  x,
  ...,
  axes = c(1, 2),
  active = TRUE,
  sup = TRUE,
  labels = FALSE,
  extra_quali = NULL,
  extra_quanti = NULL,
  ellipse = NULL,
  hull = NULL,
  color = NULL,
  fill = FALSE,
  symbol = FALSE,
  size = c(1, 6),
  xlim = NULL,
  ylim = NULL,
  main = NULL,
  sub = NULL,
  panel.first = NULL,
  panel.last = NULL,
  legend = list(x = "topleft")
)

Arguments

x

A CA, MCA or PCA object.

...

Further graphical parameters.

axes

A length-two numeric vector giving the dimensions to be plotted.

active

A logical scalar: should the active observations be plotted?

sup

A logical scalar: should the supplementary observations be plotted?

labels

A logical scalar: should labels be drawn? Labeling a large number of points can be computationally expensive and make the graph difficult to read. A selection of points to label can be provided using a list of two named elements, filter (a string specifying how to filter the labels to be drawn) and n (an integer specifying the number of labels to be drawn). See examples below.

extra_quali

An optional vector of qualitative data for aesthetics mapping.

extra_quanti

An optional vector of quantitative data for aesthetics mapping. If a single character string is passed, it must be one of "observation", "mass", "sum", "contribution" or "cos2" (see augment()).

ellipse

A list of additional arguments to be passed to viz_ellipses(); names of the list are used as argument names. If NULL, no ellipse are displayed.

hull

A logical scalar: should convex hulls be displayed?

color

The colors for lines and points (will be mapped to extra_quanti or extra_quali; if both are set, the latter has priority). Ignored if set to FALSE. If NULL, the default color scheme will be used.

fill

The background colors for points (will be mapped to extra_quanti or extra_quali; if both are set, the latter has priority). Ignored if set to FALSE.

symbol

A vector of plotting characters or symbols (will be mapped to extra_quali). This can either be a single character or an integer code for one of a set of graphics symbols. If symbol is a named a named vector, then the symbols will be associated with their name within extra_quali. Ignored if set to FALSE.

size

A length-two numeric vector giving range of possible sizes (greater than 0; will be mapped to extra_quanti). Ignored if set to FALSE.

xlim

A length-two numeric vector giving the x limits of the plot. The default value, NULL, indicates that the range of the finite values to be plotted should be used.

ylim

A length-two numeric vector giving the y limits of the plot. The default value, NULL, indicates that the range of the finite values to be plotted should be used.

main

A character string giving a main title for the plot.

sub

A character string giving a subtitle for the plot.

panel.first

An expression to be evaluated after the plot axes are set up but before any plotting takes place. This can be useful for drawing background grids.

panel.last

An expression to be evaluated after plotting has taken place but before the axes, title and box are added.

legend

A list of additional arguments to be passed to graphics::legend(); names of the list are used as argument names. If NULL, no legend is displayed.

Value

⁠viz_*()⁠ is called for its side-effects: it results in a graphic being displayed. Invisibly returns x.

Author(s)

N. Frerebeau

See Also

Other plot methods: biplot(), plot(), screeplot(), viz_contributions(), viz_variables()

Examples

## Load data
data("iris")

## Compute principal components analysis
X <- pca(iris, scale = TRUE, sup_quali = "Species")

## Plot individuals
viz_individuals(X, panel.last = graphics::grid())

## Labels of the 10 individuals with highest cos2
viz_individuals(X, labels = list(filter = "cos2", n = 10))

## Plot variables
viz_variables(X, panel.last = graphics::grid())

## Graphical parameters
## Continuous values
viz_individuals(X, extra_quanti = iris$Petal.Length, symbol = 16, size = c(1, 2))
viz_individuals(X, extra_quanti = iris$Petal.Length, symbol = 16, size = c(1, 2),
                color = grDevices::hcl.colors(12, "RdPu"))

viz_variables(X, extra_quanti = "contribution",
              color = grDevices::hcl.colors(12, "BluGrn", rev = TRUE),
              size = c(0, 1))

## Discrete values
viz_individuals(X, extra_quali = iris$Species, symbol = 21:23)
viz_individuals(X, extra_quali = iris$Species, symbol = 21:23,
                fill = c("#004488", "#DDAA33", "#BB5566"),
                color = "black")

viz_variables(X, extra_quali = c("Petal", "Petal", "Sepal", "Sepal"),
              color = c("#EE7733", "#0077BB"),
              symbol = c(1, 3))

Non-Overlapping Text Labels

Description

Non-Overlapping Text Labels

Usage

viz_labels(x, filter = "contribution", n = 10, type = "shadow", ...)

Arguments

x

A data.frame (typically returned by prepare_plot()).

filter

A character string specifying the variable used to filter observations. If NULL, all labels are drawn.

n

An integer specifying the number of labels to draw. Only the labels of the top n observations according to filter will be drawn. If NULL, all labels are drawn.

type

A character string specifying the shape of the field. It must be one of "text", "shadow" or "box". Any unambiguous substring can be given.

...

Currently not used.

Details

Only labels in the plotting region (given by par("usr")) will be drawn.

Author(s)

N. Frerebeau

Build a Factor Map

Description

Build a Factor Map

Usage

viz_points(
  x,
  margin,
  axes,
  ...,
  active = TRUE,
  sup = TRUE,
  labels = list(filter = "contribution", n = 10),
  extra_quali = NULL,
  extra_quanti = NULL,
  color = NULL,
  fill = FALSE,
  symbol = NULL,
  size = c(1, 6),
  xlim = NULL,
  ylim = NULL,
  main = NULL,
  sub = NULL,
  xlab = NULL,
  ylab = NULL,
  ann = graphics::par("ann"),
  frame.plot = TRUE,
  panel.first = NULL,
  panel.last = NULL,
  ellipse = NULL,
  hull = FALSE,
  legend = list(x = "topleft")
)

Arguments

x

A CA, MCA or PCA object.

margin

A length-one numeric vector giving the subscript which the data will be returned: 1 indicates individuals/rows (the default), 2 indicates variables/columns.

axes

A length-two numeric vector giving the dimensions to be plotted.

...

Currently not used.

active

A logical scalar: should the active observations be plotted?

sup

A logical scalar: should the supplementary observations be plotted?

labels

A logical scalar: should labels be drawn? Labeling a large number of points can be computationally expensive and make the graph difficult to read. A selection of points to label can be provided using a list of two named elements, filter (a string specifying how to filter the labels to be drawn) and n (an integer specifying the number of labels to be drawn). See examples below.

extra_quali

An optional vector of qualitative data for aesthetics mapping.

extra_quanti

An optional vector of quantitative data for aesthetics mapping. If a single character string is passed, it must be one of "observation", "mass", "sum", "contribution" or "cos2" (see augment()).

color

The colors for lines and points (will be mapped to extra_quanti or extra_quali; if both are set, the latter has priority). Ignored if set to FALSE. If NULL, the default color scheme will be used.

fill

The background colors for points (will be mapped to extra_quanti or extra_quali; if both are set, the latter has priority). Ignored if set to FALSE.

symbol

A vector of plotting characters or symbols (will be mapped to extra_quali). This can either be a single character or an integer code for one of a set of graphics symbols. If symbol is a named a named vector, then the symbols will be associated with their name within extra_quali. Ignored if set to FALSE.

size

A length-two numeric vector giving range of possible sizes (greater than 0; will be mapped to extra_quanti). Ignored if set to FALSE.

xlim

A length-two numeric vector giving the x limits of the plot. The default value, NULL, indicates that the range of the finite values to be plotted should be used.

ylim

A length-two numeric vector giving the y limits of the plot. The default value, NULL, indicates that the range of the finite values to be plotted should be used.

main

A character string giving a main title for the plot.

sub

A character string giving a subtitle for the plot.

xlab, ylab

A character vector giving the x and y axis labels.

ann

A logical scalar: should the default annotation (title and x and y axis labels) appear on the plot?

frame.plot

A logical scalar: should a box be drawn around the plot?

panel.first

An expression to be evaluated after the plot axes are set up but before any plotting takes place. This can be useful for drawing background grids.

panel.last

An expression to be evaluated after plotting has taken place but before the axes, title and box are added.

ellipse

A list of additional arguments to be passed to viz_ellipses(); names of the list are used as argument names. If NULL, no ellipse are displayed.

hull

A logical scalar: should convex hulls be displayed?

legend

A list of additional arguments to be passed to graphics::legend(); names of the list are used as argument names. If NULL, no legend is displayed.

Author(s)

N. Frerebeau

Tolerance Ellipses

Description

Plots tolerance ellipses.

Usage

viz_tolerance(x, y, ...)

wrap_tolerance(x, y, ...)

## S4 method for signature 'numeric,numeric'
viz_tolerance(
  x,
  y,
  ...,
  group = NULL,
  level = 0.95,
  color = NULL,
  fill = FALSE,
  symbol = FALSE
)

## S4 method for signature 'MultivariateAnalysis,missing'
viz_tolerance(
  x,
  ...,
  group = NULL,
  level = 0.95,
  color = NULL,
  fill = FALSE,
  symbol = FALSE
)

## S4 method for signature 'MultivariateBootstrap,missing'
viz_tolerance(
  x,
  ...,
  level = 0.95,
  color = FALSE,
  fill = FALSE,
  symbol = FALSE
)

## S4 method for signature 'PCOA,missing'
viz_tolerance(
  x,
  ...,
  group = NULL,
  level = 0.95,
  color = NULL,
  fill = FALSE,
  symbol = FALSE
)

## S4 method for signature 'numeric,numeric'
wrap_tolerance(x, y, group = NULL, level = 0.95)

## S4 method for signature 'MultivariateAnalysis,missing'
wrap_tolerance(
  x,
  margin = 1,
  axes = c(1, 2),
  group = NULL,
  level = 0.95,
  principal = TRUE
)

## S4 method for signature 'PCOA,missing'
wrap_tolerance(x, axes = c(1, 2), group = NULL, level = 0.95)

Arguments

x, y

A numeric vector. If y is missing, x must be an object from which to wrap observations (a CA, MCA or PCA object).

...

Further graphical parameters to be passed to graphics::polygon().

group

A vector specifying the group an observation belongs to.

level

A numeric vector specifying the confidence/tolerance level.

color

The colors for borders (will be mapped to group). Ignored if set to FALSE. If NULL, the default color scheme will be used.

fill

The background colors (will be mapped to group). Ignored if set to FALSE.

symbol

A vector of symbols (will be mapped to group). Ignored if set to FALSE.

margin

A length-one numeric vector giving the subscript which the data will be returned: 1 indicates individuals/rows (the default), 2 indicates variables/columns.

axes

A length-two numeric vector giving the dimensions for which to compute results.

principal

A logical scalar: should principal coordinates be used? If FALSE, standard coordinates are returned.

Value

wrap_tolerance() returns a data.frame of envelope x and y coordinates. An extra column named group is added specifying the group an observation belongs to.

viz_tolerance()is called for its side-effects: it results in a graphic being displayed. Invisibly returns x.

Author(s)

N. Frerebeau

See Also

Other envelopes: viz_confidence(), viz_ellipses(), viz_hull()

Examples

## Load data
data("iris")

## Compute principal components analysis
X <- pca(iris, scale = TRUE, sup_quali = "Species")

## Compute tolerance ellipse coordinates
conf <- wrap_confidence(X, margin = 1, group = "Species", level = 0.95)

## Plot tolerance ellipses
col <- c("#004488", "#DDAA33", "#BB5566")
viz_rows(X, extra_quali = iris$Species, color = col)
viz_tolerance(X, group = iris$Species, color = col, level = 0.95)

Visualize Variables Factor Map

Description

Plots column/variable principal coordinates.

Usage

viz_variables(x, ...)

viz_columns(x, ...)

## S4 method for signature 'MultivariateAnalysis'
viz_columns(
  x,
  ...,
  axes = c(1, 2),
  active = TRUE,
  sup = TRUE,
  labels = FALSE,
  extra_quali = NULL,
  extra_quanti = NULL,
  color = NULL,
  fill = FALSE,
  symbol = FALSE,
  size = c(1, 6),
  xlim = NULL,
  ylim = NULL,
  main = NULL,
  sub = NULL,
  panel.first = NULL,
  panel.last = NULL,
  legend = list(x = "topleft")
)

## S4 method for signature 'MultivariateBootstrap'
viz_columns(
  x,
  ...,
  axes = c(1, 2),
  color = FALSE,
  fill = FALSE,
  symbol = FALSE,
  legend = NULL
)

## S4 method for signature 'PCA'
viz_variables(
  x,
  ...,
  axes = c(1, 2),
  active = TRUE,
  sup = TRUE,
  labels = list(filter = "contribution", n = 10),
  extra_quali = NULL,
  extra_quanti = NULL,
  color = NULL,
  symbol = NULL,
  size = 1,
  xlim = NULL,
  ylim = NULL,
  main = NULL,
  sub = NULL,
  panel.first = NULL,
  panel.last = NULL,
  legend = list(x = "topleft")
)

## S4 method for signature 'CA'
viz_variables(
  x,
  ...,
  axes = c(1, 2),
  active = TRUE,
  sup = TRUE,
  labels = FALSE,
  extra_quali = NULL,
  extra_quanti = NULL,
  color = NULL,
  fill = FALSE,
  symbol = FALSE,
  size = c(1, 6),
  xlim = NULL,
  ylim = NULL,
  main = NULL,
  sub = NULL,
  panel.first = NULL,
  panel.last = NULL,
  legend = list(x = "topleft")
)

## S4 method for signature 'BootstrapPCA'
viz_variables(
  x,
  ...,
  axes = c(1, 2),
  color = FALSE,
  fill = FALSE,
  symbol = FALSE,
  legend = NULL
)

Arguments

x

A CA, MCA or PCA object.

...

Further graphical parameters.

axes

A length-two numeric vector giving the dimensions to be plotted.

active

A logical scalar: should the active observations be plotted?

sup

A logical scalar: should the supplementary observations be plotted?

labels

A logical scalar: should labels be drawn? Labeling a large number of points can be computationally expensive and make the graph difficult to read. A selection of points to label can be provided using a list of two named elements, filter (a string specifying how to filter the labels to be drawn) and n (an integer specifying the number of labels to be drawn). See examples below.

extra_quali

An optional vector of qualitative data for aesthetics mapping.

extra_quanti

An optional vector of quantitative data for aesthetics mapping. If a single character string is passed, it must be one of "observation", "mass", "sum", "contribution" or "cos2" (see augment()).

color

The colors for lines and points (will be mapped to extra_quanti or extra_quali; if both are set, the latter has priority). Ignored if set to FALSE. If NULL, the default color scheme will be used.

fill

The background colors for points (will be mapped to extra_quanti or extra_quali; if both are set, the latter has priority). Ignored if set to FALSE.

symbol

A vector of plotting characters or symbols (will be mapped to extra_quali). This can either be a single character or an integer code for one of a set of graphics symbols. If symbol is a named a named vector, then the symbols will be associated with their name within extra_quali. Ignored if set to FALSE.

size

A length-two numeric vector giving range of possible sizes (greater than 0; will be mapped to extra_quanti). Ignored if set to FALSE.

xlim

A length-two numeric vector giving the x limits of the plot. The default value, NULL, indicates that the range of the finite values to be plotted should be used.

ylim

A length-two numeric vector giving the y limits of the plot. The default value, NULL, indicates that the range of the finite values to be plotted should be used.

main

A character string giving a main title for the plot.

sub

A character string giving a subtitle for the plot.

panel.first

An expression to be evaluated after the plot axes are set up but before any plotting takes place. This can be useful for drawing background grids.

panel.last

An expression to be evaluated after plotting has taken place but before the axes, title and box are added.

legend

A list of additional arguments to be passed to graphics::legend(); names of the list are used as argument names. If NULL, no legend is displayed.

Value

⁠viz_*()⁠ is called for its side-effects: it results in a graphic being displayed. Invisibly returns x.

Author(s)

N. Frerebeau

See Also

Other plot methods: biplot(), plot(), screeplot(), viz_contributions(), viz_individuals()

Examples

## Load data
data("iris")

## Compute principal components analysis
X <- pca(iris, scale = TRUE, sup_quali = "Species")

## Plot individuals
viz_individuals(X, panel.last = graphics::grid())

## Labels of the 10 individuals with highest cos2
viz_individuals(X, labels = list(filter = "cos2", n = 10))

## Plot variables
viz_variables(X, panel.last = graphics::grid())

## Graphical parameters
## Continuous values
viz_individuals(X, extra_quanti = iris$Petal.Length, symbol = 16, size = c(1, 2))
viz_individuals(X, extra_quanti = iris$Petal.Length, symbol = 16, size = c(1, 2),
                color = grDevices::hcl.colors(12, "RdPu"))

viz_variables(X, extra_quanti = "contribution",
              color = grDevices::hcl.colors(12, "BluGrn", rev = TRUE),
              size = c(0, 1))

## Discrete values
viz_individuals(X, extra_quali = iris$Species, symbol = 21:23)
viz_individuals(X, extra_quali = iris$Species, symbol = 21:23,
                fill = c("#004488", "#DDAA33", "#BB5566"),
                color = "black")

viz_variables(X, extra_quali = c("Petal", "Petal", "Sepal", "Sepal"),
              color = c("#EE7733", "#0077BB"),
              symbol = c(1, 3))

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