mjca {ca} | R Documentation |
Computation of multiple and joint correspondence analysis.
mjca(obj, nd = 2, lambda = "adjusted", supcol = NA, maxit = 50, epsilon = 0.0001)
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). |
The function mjca
computes a multiple or joint correspondence analysis based on the eigenvalue decomposition of the Burt matrix.
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 |
eigen
, plot.mjca
, summary.mjca
, print.mjca
library(MASS) data(farms) mjca(farms) # Joint correspondence analysis: mjca(farms, lambda = "JCA")