mjca {ca}R Documentation

Multiple and joint correspondence analysis

Description

Computation of multiple and joint correspondence analysis.

Usage

mjca(obj, nd = 2, lambda = "adjusted", supcol = NA, maxit = 50, epsilon = 0.0001)

Arguments

obj A response pattern matrix containing factors.
nd Number of dimensions to be included in the output; if NA the maximum possible dimensions are included.
lambda Gives the scaling method. Possible values include "indicator", "Burt", "adjusted" and "JCA". Using lambda = "JCA" results in a joint correspondence analysis using iterative adjusment of the Burt matrix in the solution space.
supcol Indices of supplementary columns.
maxit The maximum number of iterations (Joint Correspondence Analysis).
epsilon A convergence criterion (Joint Correspondence Analysis).

Details

The function mjca computes a multiple or joint correspondence analysis based on the eigenvalue decomposition of the Burt matrix.

Value

sv Eigenvalues (lambda = "indicator") or singular values (lambda = "Burt", "adjusted" or "JCA")
lambda Scaling method
inertia.e Percentages of explained inertia
inertia.t Total inertia
inertia.et Total percentage of explained inertia with the nd-dimensional solution
levelnames Names of the factor/level combinations
levels.n Number of levels in each factor
nd User-specified dimensionality of the solution
nd.max Maximum possible dimensionality of the solution
rownames Row names
rowmass Row masses
rowdist Row chi-square distances to centroid
rowinertia Row inertias
rowcoord Row standard coordinates
colnames Column names
colmass Column masses
coldist Column chi-square distances to centroid
colinertia Column inertias
colcoord Column standard coordinates
colsup Indices of column supplementary points (of the Burt and Indicator matrix)
Burt Burt matrix
Burt.upd The updated Burt matrix (JCA only)
subinertia Inertias of sub-matrices
JCA.iter Vector of length two containing the number of iterations and the epsilon (JCA only)
call Return of match.call

See Also

eigen, plot.mjca, summary.mjca, print.mjca

Examples

 
library(MASS)
data(farms)
mjca(farms)

# Joint correspondence analysis:
mjca(farms, lambda = "JCA")

 

[Package ca version 0.21 Index]