partial least squares regression, projection to latent structure regression or PLSR computed with the standard algorithm
[model, rmsec, rmsecv, b,ypredcv]=pls(x,y,split,lv,(centred))
calibration dataset; a matrix (n x q) and a vector (n x 1) or Div structures
parameter for the cross-validation:
- an integer: number of random blocs
- two integers [a b]: a random blocks ; b repetitions of the cross-validation
- a vector of dimension n attributing each observation to a block (numbers 1,2,...k for k blocks): blocks given by the vector
- a matrix (n x b ) of column-vectors of dimension n attributing each observation to a block (numbers 1,2,...k for k blocs): blocks given by each vector, b repetitions of the cross-validation
- 'vnbxx': venitian blinds, xx blocks; ex: 'vnb10' for 10 blocks
- 'jckxx': Jack knife, xx blocks; ex: 'jck8' for 8 blocks
number of latent variables
centering=1 (by default); no centering = 0
the standard errors of calibration aand cross-validation
model.err.d is a matrix (lv x 2); the columns are the rmsec and the rmsecv respectively
y predicted after cross validation for each of the latent variables
model.ypredcv.d is a matrix of dimensions (n x lv)
the b coefficients or regression coefficients
model.b.d is a matrix of dimensions (q x lv)
the scores of the observations onto the loadings
model.scores.d is a matrix of dimensions (n x lv)
the loadings
model.loadings.d is a matrix of dimensions (q x lv)
means of x and y; a vector (q x 1) and a scalar
1 = centred, 0 = not centred
rmsec=model.rmsec.d
rmsecv=model.rmsecv.d
b=model.b.d
ypredcv=model.ypredcv.d
model.x_ref
model.y_ref