coiMCA | R Documentation |
Coinertia analysis between two groups of categorical variables
coiMCA(Xa, Xb,
excl.a = NULL, excl.b = NULL,
row.w = NULL, ncp = 5)
Xa |
data frame with the first group of categorical variables |
Xb |
data frame with the second group of categorical variables |
excl.a |
numeric vector indicating the indexes of the "junk" categories in |
excl.b |
numeric vector indicating the indexes of the "junk" categories in |
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) |
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.
With categorical data, it consists in the following steps :
1. Transformation of Xa
and Xb
into indicator matrices (i.e. disjunctive tables) Xad
and Xbd
2. Computation of the covariance matrix t(Xad).Xbd
3. CA of the matrix
An object of class CA
from FactoMineR
package, with an additional item :
RV |
the RV coefficient between the two groups of variabels |
Nicolas Robette
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.
coiPCA
, rvcoef
data(Music)
# music tastes
Xa <- Music[,1:5]
# gender and age
Xb <- Music[,6:7]
# coinertia analysis
res <- coiMCA(Xa, Xb)
plot(res)
# RV coefficient
res$RV
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