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fact >> (X1-X2…Xk) Multi-way analysis > ccswa

ccswa

common components and specific weights analysis

Calling sequence

result = ccswa(col,lv);

Arguments

col:

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

lv:

number of components; the dimension max of the model

result.global.scores:

scores of the compromise

result.global.scores.d is a matrix of dimensions (n x lv)

result.global.weights:

weight of each table according to the number of components

result.global.weights.d is a matrix of dimensions (ntab x lv)

result.individual.scores:

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)

result.individual.loadings:

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)

result.individual.tablenorm:

Frobenius norm of each table

result.individual.mean:

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)

Examples

[res]=ccswa(col,10)

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