pca {dimensio} | R Documentation |
Principal Components Analysis
Description
Computes a principal components analysis based on the singular value decomposition.
Usage
pca(object, ...)
## S4 method for signature '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 signature 'matrix'
pca(
object,
center = TRUE,
scale = TRUE,
rank = NULL,
sup_row = NULL,
sup_col = NULL,
weight_row = NULL,
weight_col = NULL
)
Arguments
object |
A |
... |
Currently not used. |
center |
A |
scale |
A |
rank |
An |
sup_row |
A |
sup_col |
A |
sup_quali |
A |
weight_row |
A |
weight_col |
A |
Value
A PCA
object.
Author(s)
N. Frerebeau
References
Lebart, L., Piron, M. and Morineau, A. Statistique exploratoire multidimensionnelle: visualisation et inférence en fouille de données. Paris: Dunod, 2006.
See Also
Other multivariate analysis:
ca()
,
mca()
,
predict()
Examples
## Load data
data("iris")
## Compute principal components analysis
X <- pca(iris, scale = TRUE, sup_row = sample(150, 10), sup_col = 2)
## Get row coordinates
head(get_coordinates(X, margin = 1))
## Get column coordinates
head(get_coordinates(X, margin = 2))
## Get correlations between variables and dimensions
head(get_correlations(X))
## Get eigenvalues
get_eigenvalues(X)