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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

pca(object, ...)

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

Arguments

object

A \(m \times p\) 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.

Value

A PCA object.

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(), predict()

Author

N. Frerebeau

Examples

## Load data
data("iris")

## Compute principal components analysis
X <- pca(iris, scale = TRUE, sup_row = sample(150, 10), sup_col = 2)
#> 1 qualitative variable was removed: Species.

## Get row coordinates
head(get_coordinates(X, margin = 1))
#>          F1          F2  .sup
#> 1 -2.119908  0.30504514 FALSE
#> 2 -2.258782  0.10373707 FALSE
#> 3 -2.431802 -0.08324361 FALSE
#> 4 -2.432948 -0.21255244 FALSE
#> 5 -2.189345  0.20439110 FALSE
#> 6 -1.655423  0.42376563 FALSE

## Get column coordinates
head(get_coordinates(X, margin = 2))
#>                      F1         F2  .sup
#> Sepal.Length  0.9309472  0.3639030 FALSE
#> Petal.Length  0.9843042 -0.1113731 FALSE
#> Petal.Width   0.9650983 -0.2374364 FALSE
#> Sepal.Width  -0.3010865  0.4476798  TRUE

## Get correlations between variables and dimensions
head(get_correlations(X))
#>                      F1         F2  .sup
#> Sepal.Length  0.9309472  0.3639030 FALSE
#> Petal.Length  0.9843042 -0.1113731 FALSE
#> Petal.Width   0.9650983 -0.2374364 FALSE
#> Sepal.Width  -0.3010865  0.4476798  TRUE

## Get eigenvalues
get_eigenvalues(X)
#>    eigenvalues  variance cumulative
#> F1   2.7669322 93.221156   93.22116
#> F2   0.2012054  6.778844  100.00000