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statislda

Statis-linear discriminant analysis method

Calling sequence

result = statislda(Xtab,Y,(RV));

Arguments

Xtab:

the different datasets, as a list

Xtab(i) is a matrix of dimensions (n x pi) or a Div structure

the sum of the pi yields p

Y:

the class of each observation

Y is a conjunctive vector or a disjunctive matrix or a Div structure of conjunctive or disjunctive data

(RV):

t: normalization of the matrix C

f: no normalization (by default)

result.p:

axes of Statis-LDA

result.p.d is a matrix of dimensions (p x n_axes)

result.t:

the scores of the observations on the axes of Statis-LDA

result.t.d is a matrix of dimensions (n x n_axes)

result.tg:

the scores of the barycenters of the q tables

result.tg.d is a matrix of dimensions (q x n_axes)

result.normw:

the norms of the q operators associated to the LDA of (Xi, Y)

result.normw.i is a vector of dimensions (q x 1)

result.cmatrix:

matrix of the inner products between the q operators

result.cmatrix.d is a matrix of dimensions (q x q)

result.cmatrix_eigenvec:

eigenvectors of cmatrix

result.cmatrix_eigenvec.d is a matrix of dimensions (q x q)

result.cc:

coefficients of the compromise after diagonalization of cmatrix

result.cc.d is a vector of dimensions (q x 1)

result.vtot:

the variance-covariance matrix of the compromise

result.vtot.d is a matrix of dimensions (p x p)

result.vtot_eigenvec:

eigenvectors of vtot

result.vtot_eigenvec.d is a matrix of dimensions (p x n_axes)

result.vtot_eigenval:

eigenvalues of vtot

result.vtot_eigenval.d is a vector of dimensions (n_axes x 1)

result.xconc_mean:

mean of the data merged by columns

result.xconc_mean.d is a vector of dimensions (p x 1)

Examples

[res]=statislda(x,y);

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