wcPCA {GDAtools}

Within-class Principal Component Analysis

Description

Within-class Principal Component Analysis

Usage

wcPCA(X, class, scale.unit = TRUE, ncp = 5, ind.sup = NULL, quanti.sup = NULL, 
          quali.sup = NULL, row.w = NULL, col.w = NULL, graph = FALSE, 
          axes = c(1, 2))

Arguments

X

a data frame with n rows (individuals) and p columns (numeric variables)

class

factor specifying the class

scale.unit

a boolean, if TRUE (default) then data are scaled to unit variance

ncp

number of dimensions kept in the results (by default 5)

ind.sup

a vector indicating the indexes of the supplementary individuals

quanti.sup

a vector indicating the indexes of the quantitative supplementary variables

quali.sup

a vector indicating the indexes of the categorical supplementary variables

row.w

an optional row weights (by default, a vector of 1 for uniform row weights); the weights are given only for the active individuals

col.w

an optional column weights (by default, uniform column weights); the weights are given only for the active variables

graph

boolean, if TRUE a graph is displayed. Default is FALSE.

axes

a length 2 vector specifying the components to plot

Details

Within-class Principal Component Analysis is a PCA where the active variables are centered on the mean of their class instead of the overall mean.

It is a "conditional" PCA and can be seen as a special case of PCA with orthogonal instrumental variables, with only one (categorical) instrumental variable.

Value

An object of class PCA from FactoMineR package, with an additional item :

ratio

the within-class inertia percentage

.

Note

The code is adapted from PCA function from FactoMineR package.

Author(s)

Nicolas Robette

References

Escofier B., 1990, Analyse des correspondances multiples conditionnelle, La revue de Modulad, 5, 13-28.

Lebart L., Morineau A. et Warwick K., 1984, Multivariate Descriptive Statistical Analysis, John Wiley and sons, New-York.)

See Also

PCAoiv, wcMCA, MCAoiv

Examples

# within-class analysis of decathlon data
# with quatiles of points as class
library(FactoMineR)
data(decathlon)
points <- cut(decathlon$Points, c(7300, 7800, 8000, 8120, 8900), c("Q1","Q2","Q3","Q4"))
res <- wcPCA(decathlon[,1:10], points)
plot(res, choix = "var")