calculates the eigenvectors and the parameters for tuning Error Removal by Orthogonal Substraction (EROS)
res = eros(x_ed,classes_indiv,xcal,ycal,split,lv,(centering))
a matrix (n1 x q) or a Div structure from an experimental design
a conjunctive vector or a disjunctive matrix identifying the different individuals of x_ed for which observations were acquired
calibration dataset; a matrix of spectra (n x q) and a vector of reference values (n x 1) or Div structures
for the cross validation; a scalar representing a number of contiguous blocks, or a vector identifying each observation to a block
number of latent variables for the PLS regression
centred=1 (by default); not centred=0
the D matrix obtained as the sum of the variance-covariance matrices of each sample after centering
res.d_matrix.d is a matrix of dimensions (q x q)
eigenvectors of d_matrix
res.eigenvec.d is a matrix of dimensions (q x (n_eigenvect))
eigenvalues of d_matrix in percent
res.ev_pcent.d is a vector of dimensions ((n_eigenvect x 1)
Wilks lambda
res.Wilks.d is a vector of dimensions ((n_eigenvect+1) x 1)
rmsecv for several dimensions of EROS and several dimensions of PLSR
res.rmsecv.d is a matrix of dimensions (lv x n_eigenvect+1)
PLS models obtained after an EROS correction using 0/1/2/...(n_eigenvect) eigenvectors from res.eigenvect.d
res.pls_models is a list of dimensions (n_eigenvect+1)
help pls for more information about the fields of res.pls_models