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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 class 'CompositionMatrix'
pca(
  object,
  center = TRUE,
  scale = FALSE,
  rank = NULL,
  sup_row = NULL,
  sup_col = NULL,
  weight_row = NULL,
  weight_col = NULL
)

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

Arguments

object

A CompositionMatrix or 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 vector specifying the indices of the supplementary rows.

sup_col

A vector specifying the indices of the supplementary 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 dimensio::PCA object. See dimensio::pca() for details.

Methods (by class)

  • pca(CompositionMatrix): PCA of centered log-ratio, i.e. log-ratio analysis (LRA).

References

Aitchison, J. and Greenacre, M. (2002). Biplots of compositional data. Journal of the Royal Statistical Society: Series C (Applied Statistics), 51: 375-392. doi:10.1111/1467-9876.00275 .

Filzmoser, P., Hron, K. and Reimann, C. (2009). Principal component analysis for compositional data with outliers. Environmetrics, 20: 621-632. doi:10.1002/env.966 .

See also

dimensio::pca(), dimensio::biplot(), dimensio::screeplot(), dimensio::viz_individuals(), dimensio::viz_variables()

Author

N. Frerebeau

Examples

## Data from Day et al. 2011
data("kommos", package = "folio") # Coerce to compositional data
kommos <- remove_NA(kommos, margin = 1) # Remove cases with missing values
coda <- as_composition(kommos, groups = 1) # Use ceramic types for grouping

## Log-Ratio Analysis
X <- pca(coda)
#> PCA of centered log-ratio (CLR).

## Biplot
biplot(X)


## Explore results
viz_individuals(X)

viz_variables(X)