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Principal Components Analysis

Source: R/AllGenerics.R, R/pca.R
pca.Rd

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

Usage

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

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

Arguments

object

A LogRatio object.

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 numeric or logical vector specifying the indices of the supplementary rows (individuals).

sup_col

A numeric or logical vector specifying the indices of the supplementary columns (variables).

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 dimensio::PCA object.

See also

dimensio::pca(), dimensio::biplot()

Author

N. Frerebeau

Examples

## Coerce to compositional data
data("hongite")
coda <- as_composition(hongite)

## Centered log-ratio
clr <- transform_clr(coda)

## PCA
X <- pca(clr)

## Plot
plot_individuals(X)