dimensio (version 0.9.0)

pca: Principal Components Analysis

Description

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

Usage

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

Value

A PCA object.

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.

Author

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)