GDAtools (version 2.1)

coiPCA: Coinertia analysis between two groups of numerical variables

Description

Coinertia analysis between two groups of numerical variables

Usage

coiPCA(Xa, Xb, row.w = NULL, ncp = 5)

Value

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

RV

the RV coefficient between the two groups of variabels

Arguments

Xa

data frame with the first group of numerical variables

Xb

data frame with the second group of numerical variables

row.w

numeric vector of row weights. If NULL (default), a vector of 1 for uniform row weights is used.

ncp

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

Author

Nicolas Robette

Details

Coinertia analysis aims at capturing the structure common to two groups of variables. With groups of numerical variables, it is equivalent to Tucker's inter-battery analysis. It consists in the following steps : 1. Variables in Xa and Xb are centered and scaled 2. Computation of the covariance matrix t(Xa).Xb 3. PCA of the matrix

References

Tucker, L.R. (1958) An inter-battery method of factor analysis. Psychometrika, 23-2, 111-136.

Dolédec, S. and Chessel, D. (1994) Co-inertia analysis: an alternative method for studying species-environment relationships. Freshwater Biology, 31, 277–294.

See Also

coiMCA, rvcoef

Examples

library(FactoMineR)
data(decathlon)
# variables of results for each sport 
Xa <- decathlon[,1:10]
# rank and points variables
Xb <- decathlon[,11:12]
# coinertia analysis
res <- coiPCA(Xa, Xb)
# plot of variables in Xa
plot(res, choix = "ind")
# plot of variables in Xb
plot(res, choix = "var")
# RV coefficient
res$RV