An S4 class to store the results of a principal components analysis.
Note
This class inherits from MultivariateAnalysis
.
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 -2.061855 0.29138817 FALSE
#> 2 -2.198033 0.09377921 FALSE
#> 3 -2.367672 -0.08953188 FALSE
#> 4 -2.368839 -0.21693207 FALSE
#> 5 -2.129944 0.19258369 FALSE
#> 6 -1.603889 0.40602302 FALSE
## Get column coordinates
head(get_coordinates(X, margin = 2))
#> F1 F2 .sup
#> Sepal.Length 0.9317843 0.3618863 FALSE
#> Petal.Length 0.9846774 -0.1126117 FALSE
#> Petal.Width 0.9661434 -0.2342445 FALSE
#> Sepal.Width -0.3250032 0.4569390 TRUE
## Get correlations between variables and dimensions
head(get_correlations(X))
#> F1 F2 .sup
#> Sepal.Length 0.9317843 0.3618863 FALSE
#> Petal.Length 0.9846774 -0.1126117 FALSE
#> Petal.Width 0.9661434 -0.2342445 FALSE
#> Sepal.Width -0.3250032 0.4569390 TRUE
## Get eigenvalues
get_eigenvalues(X)
#> eigenvalues variance cumulative
#> F1 2.7712448 93.315497 93.3155
#> F2 0.1985136 6.684503 100.0000