Package 'dimensio'

Title: Multivariate Data Analysis
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).
Authors: Nicolas Frerebeau [aut, cre] , Jean-Baptiste Fourvel [ctb] , Brice Lebrun [ctb] (<https://orcid.org/0000-0001-7503-8685>, Logo designer), Université Bordeaux Montaigne [fnd], CNRS [fnd]
Maintainer: Nicolas Frerebeau <[email protected]>
License: GPL (>= 3)
Version: 0.10.0
Built: 2024-11-08 10:14:53 UTC
Source: https://github.com/tesselle/dimensio

Help Index

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", "#009E73"),
  col.columns = c("#56B4E9", "#F0E442"),
  cex.rows = graphics::par("cex"),
  cex.columns = graphics::par("cex"),
  pch.rows = 16,
  pch.columns = 17,
  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", "#009E73"),
  col.columns = c("#56B4E9", "#F0E442"),
  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

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

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

A symbol specification.
cex, cex.rows, cex.columns

A numeric vector giving the amount by which plotting characters and symbols should be scaled relative to the default.

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.

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(), viz_wrap, wrap

Examples

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

## 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(countries, scale = FALSE, weight_row = row_w, weight_col = col_w)

## 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, margin = 1, level = c(0.68, 0.95))

viz_columns(Y)
viz_tolerance(Y, margin = 2, level = c(0.68, 0.95))

## Plot with convex hulls
viz_columns(Y)
viz_hull(Y, margin = 2)

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

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

## Plot with ellipses
viz_variables(Y)
viz_tolerance(Y, margin = 2, 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)

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

Arguments

object

A m×pm \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)−1min(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.

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

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: loadings(), 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", ...)

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)

## 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

  • get_contributions() returns a data.frame of contributions to the definition of the principal dimensions.

  • get_correlations() returns a data.frame of correlations between variables and dimensions. An extra column (named after sup_name) is added specifying whether an observation is a supplementary point or not.

  • get_cos2() returns a data.frame of cos2cos^2 values (i.e. quality of the representation of the points on the factor map). An extra column (named after sup_name) is added specifying whether an observation is a supplementary point or not.

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

  • get_coordinates() returns a data.frame of coordinates. An extra column (named after sup_name) is added specifying whether an observation is a supplementary point or not.

  • get_replications() returns an array of coordinates.

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)

## 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

  • get_eigenvalues() returns a data.frame with the following columns: eigenvalues, variance (percentage of variance) and cumulative (cumulative percentage of variance).

  • get_variance() returns a numeric vector giving the amount of variance explained by each (principal) component.

  • get_distance()returns a numeric vector of squared distance to the centroid.

  • get_inertia() returns a numeric vector giving the inertia (weighted squared distance to the centroid).

Author(s)

N. Frerebeau

See Also

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

Extract Loadings

Description

Extract loadingsin principal components analysis.

Usage

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

Arguments

x

A PCA object.
...

Currently not used.

Value

Returns variable loadings (i.e. the coefficients of the linear combination of the original variables).

Note

loadings() is only implemented for consistency with stats::loadings().

Author(s)

N. Frerebeau

See Also

Other mutators: dimnames(), subset()

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)

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

Arguments

object

A m×pm \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)−1min(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.

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
)

## 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×pm \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−1p - 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.

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)

## 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)

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,
  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
)

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()).
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.
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.

Author(s)

N. Frerebeau

See Also

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

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)

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(), viz_wrap, wrap

Examples

## Load data
data("iris")

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

## 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(), loadings()

Examples

## Load data
data("iris")

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

## 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 'CA'
summary(object, ..., margin = 1, active = TRUE, sup = TRUE, rank = 3)

## S4 method for signature 'PCA'
summary(object, ..., margin = 1, active = TRUE, sup = TRUE, rank = 3)

Arguments

object

A CA, MCA or PCA object.
...

Currently not used.
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.

Author(s)

N. Frerebeau

See Also

Other 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)

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

cos2cos^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 cos2cos^2 along axes.

Author(s)

N. Frerebeau

See Also

Other summary: summary()

Examples

## Load data
data("iris")

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

## 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)))

Visualize Contributions and cos2

Description

Plots contributions histogram and cos2cos^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 = c(1, 2),
  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(), viz_wrap, wrap

Examples

## Load data
data("iris")

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

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

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

## Plot cos2
viz_cos2(X)

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,
  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)

## S4 method for signature 'PCA'
viz_individuals(
  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")
)

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.
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(), viz_wrap, wrap

Examples

## Load data
data("iris")

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

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

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

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
)

## 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
)

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.
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(), viz_wrap, wrap

Examples

## Load data
data("iris")

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

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

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

Plot Envelopes

Description

Plot Envelopes

Usage

viz_hull(x, ...)

viz_confidence(x, ...)

viz_tolerance(x, ...)

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

## S4 method for signature 'BootstrapCA'
viz_tolerance(
  x,
  ...,
  margin = 1,
  axes = c(1, 2),
  level = 0.95,
  color = FALSE,
  fill = FALSE,
  symbol = FALSE
)

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

## S4 method for signature 'BootstrapCA'
viz_confidence(
  x,
  ...,
  margin = 1,
  axes = c(1, 2),
  level = 0.95,
  color = FALSE,
  fill = FALSE,
  symbol = FALSE
)

## S4 method for signature 'MultivariateAnalysis'
viz_hull(
  x,
  ...,
  margin = 1,
  axes = c(1, 2),
  group = NULL,
  color = NULL,
  fill = FALSE,
  symbol = FALSE
)

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

Arguments

x

An object from which to wrap observations (a CA, MCA or PCA object).
...

Further graphical parameters to be passed to graphics::polygon().
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.
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.
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_*()⁠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(), viz_variables(), wrap

Examples

## Load data
data("iris")

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

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

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

Wrap Observations

Description

  • wrap_hull() computes convex hull of a set of observations.

  • wrap_confidence() computes a confidence ellipse.

  • wrap_tolerance() computes a tolerance ellipse.

Usage

wrap_hull(x, ...)

wrap_confidence(x, ...)

wrap_tolerance(x, ...)

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

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

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

Arguments

x

An object from which to wrap observations (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.
group

A vector specifying the group an observation belongs to.
level

A numeric vector specifying the confidence/tolerance level.

Value

⁠wrap_*()⁠ returns a data.frame of envelope x and y coordinates.

An extra column named group is added specifying the group an observation belongs to.

Author(s)

N. Frerebeau

See Also

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

Examples

## Load data
data("iris")

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

## Confidence ellipse coordinates
conf <- wrap_confidence(X, margin = 1, group = "Species", level = c(0.68, 0.95))

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

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