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epo

calculates the eigenvectors and the parameters for tuning External Parameter Orthogonalization (EPO) with the classical approach based on the means for each influence factor

Calling sequence

res = epo(x_ed,classes_indiv,classes_perturb,xcal,ycal,split,lv,(centering))

Arguments

x_ed:

a matrix (n1 x q) or a Div structure from an experimental design

classes_indiv:

a conjunctive vector or a disjunctive matrix identifying the different samples of x_ed for which observations were acquired

classes_perturb:

a conjunctive vector or a disjunctive matrix identifying the different levels of the detrimental influence; their number is nbr_perturb

xcal,ycal:

calibration dataset; a matrix of spectra (n x q) and a vector of reference values (n x 1) or Div structures

split:

for the cross validation; a scalar representing a number of contiguous blocks, or a vector identifying each observation to a block

lv:

number of latent variables for the PLS regression

(centering):

centred=1 (by default); not centred=0

res.d_matrix:

the D matrix containing only detrimental information

res.d_matrix.d is a matrix of dimensions ((nbr_perturb-1) x q)

res.eigenvec:

eigenvectors of d_matrix

res.eigenvec.d is a matrix of dimensions (q x (nbr_perturb-1))

res.ev_pcent:

eigenvalues of d_matrix in percent

res.d_ev_pcent.d is a vector of dimensions ((nbr_perturb-1) x 1)

res.wilks:

Wilks lambda

res.wilks.d is a vector of dimensions (nbr_perturb x 1)

res.rmsecv:

rmsecv for several dimensions of EPO and several dimensions of PLSR

res.rmsecv.d is a matrix of dimensions (lv x nbr_perturb)

res.pls_models:

PLS models obtained after an EPO correction using 0/1/2/...(nbr_perturb - 1) eigenvectors from res.eigenvect.d

res.pls_models is a list of dimensions (nbr_perturb)

help pls for more information about the fields of res.pls_models

Examples

[res_epo]=epo(xg,cl_ech,cl_pert,xcal,ycal,10,5)
[res_epo]=epo(xg,cl_ech,cl_pert,xcal,ycal,10,5,0)

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