common components and specific weights analysis
result = ccswa(col,lv);
a vector (ntab x 1) of Div structures, each of them containing a matrix i, i=1:ntab;
all the matrices have the same number of observations n
the table i has pi variables
number of components; the dimension max of the model
scores of the compromise
result.global.scores.d is a matrix of dimensions (n x lv)
weight of each table according to the number of components
result.global.weights.d is a matrix of dimensions (ntab x lv)
scores of each table i; a vector of Div structures of dimensions (ntab x 1)
result.individual.scores(i).d is a matrix of dimensions (n x lv)
scores of each component; a vector of Div structures of dimensions (ntab x 1)
result.individual.loadings(i).d is a matrix of dimensions (pi x lv)
Frobenius norm of each table
vectors of the means of each table; a vector of Div structures of dimensions (ntab x 1)
result.individual.mean(i).d is a vector of dimensions (1 x pi)