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fact >> (X1-X2…Xk) Multi-way analysis > acom1

acom1

common components and co-inertia analysis

Calling sequence

result= acom1(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:

global scores

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

result.global_loadings:

global loadings

result.global_loadings.d is a matrix of dimensions ((sum of pi) 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; a Div structure

result.individual_tablenorm.d is a vector of length ntab

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 (pi x 1)

result.trajectory:

concatened matrix of individual scores, useful for barycenter representation; a Div structures of dimensions ((n x ntab) x lv)

result.tables_size:

the number of variables of each table; a Div structure

result.tables_size.d is a vector of dimensions (ntab x 1), containing the pi

result.tables_scores:

the scores associated to each table; a Div structure

result.tables_scores.d is a matrix of dimensions (ntab x lv), containing the pi

result.tables_corr:

correlations

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

result.explained_sum_squares:

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

Examples

[res]=acom1(col,10)

Bibliography

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