pca: Principal Components Analysis

Description

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

Usage

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

Value

A dimensio::PCA object. See dimensio::pca() for details.

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.

Methods (by class)

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

Author

N. Frerebeau

References

Aitchison, J. and Greenacre, M. (2002). Biplots of compositional data. Journal of the Royal Statistical Society: Series C (Applied Statistics), 51: 375-392. tools:::Rd_expr_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. tools:::Rd_expr_doi("10.1002/env.966").

Examples

Run this code

## 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)

## Biplot
biplot(X)

## Explore results
viz_individuals(X, extra_quali = group_names(coda))
viz_variables(X)

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