Skip to contents

PCA Results

Source: R/AllClasses.R
PCA-class.Rd

An S4 class to store the results of a principal components analysis.

Slots

center

A numeric vector giving the column mean of the initial dataset (active individuals only).

scale

A numeric vector giving the column standard deviations of the initial dataset (active individuals only).

Note

This class inherits from MultivariateAnalysis.

See also

Other class: BootstrapCA-class, BootstrapPCA-class, CA-class, MCA-class, MultivariateAnalysis, MultivariateBootstrap, MultivariateResults, MultivariateSummary

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 -1.960289  0.29289248 FALSE
#> 2 -2.095123  0.09662184 FALSE
#> 3 -2.263363 -0.08499521 FALSE
#> 4 -2.263966 -0.21243774 FALSE
#> 5 -2.027706  0.19475716 FALSE
#> 6 -1.505932  0.40745304 FALSE

## Get column coordinates
head(get_coordinates(X, margin = 2))
#>                      F1         F2  .sup
#> Sepal.Length  0.9300371  0.3664032 FALSE
#> Petal.Length  0.9837931 -0.1167922 FALSE
#> Petal.Width   0.9658332 -0.2338595 FALSE
#> Sepal.Width  -0.3266602  0.4670234  TRUE

## Get correlations between variables and dimensions
head(get_correlations(X))
#>                      F1         F2  .sup
#> Sepal.Length  0.9300371  0.3664032 FALSE
#> Petal.Length  0.9837931 -0.1167922 FALSE
#> Petal.Width   0.9658332 -0.2338595 FALSE
#> Sepal.Width  -0.3266602  0.4670234  TRUE

## Get eigenvalues
get_eigenvalues(X)
#>    eigenvalues  variance cumulative
#> F1    2.765652 93.174999     93.175
#> F2    0.202582  6.825001    100.000