calculates the tuning parameters of an orthogonal projection for which the matrix of detrimental information is given
res_dmt=pop_tune(dmatrix,maxdim,xg,classes_observ,func_reg,x,y,split,lv,(rparam),(centering))
a matrix (n1 x q) or a Div structure containing only detrimental information
the maximum number of dimensions extracted from dmatrix
a matrix or a Div structure containing detrimental information
a vector identifying the samples in xg
the chosen regression function; a string, ex: 'pls'
a calibration dataset: a matrix and a vector or Div structures
for the cross-validation; the number of blocks, or a vector attributing each observation to a CV block
number of latent variables for the regression
the r parameter; only for the function 'vodka'
centred = 1 (by default); not centred=0
the matrix containing only detrimental information
res.d_matrix.d is a matrix of dimensions ((nbr_perturb-1) x q)
the eigenvectors of d_matrix
res.d_eigenvect.d is a matrix of dimensions (q x (nbr_perturb-1))
the eigenvalues of d_matrix in percent
res.d_eigenvalpcent.d is a vector of dimensions ((nbr_perturb-1) x 1)
Wilks lambda
res.Wilks.d is a vector of dimensions (nbr_perturb x 1)
rmsecv for several dimensions of orthogonal projection and several of regression
res.rmsecv.d is a matrix of dimensions (lv x nbr_perturb)
regression models obtained after an orthogonal projection using 0/1/2/...(nbr_perturb - 1) eigenvectors of res.eigenvect.d
res.pls_models is a list of dimension (nbr_perturb)
help pls for more information about the fields of res.pls_models