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spls

stacked PLS regression

Calling sequence

model=spls(x,y,lv,obs_split,var_split,(centred))

Arguments

x, y :

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

lv :

number of latent variables

obs_split:

parameter for the cross-validation:

- an integer: number of random blocs

- two integers [a b]: a random blocs ; b repetitions of the cross-validation

- a vector of dimension n attributing each observation to a bloc (numbers 1,2,...k for k blocs): blocs given by the vector

- a matrix (n x b ) of column-vectors of dimension n attributing each observation to a bloc (numbers 1,2,...k for k blocs): blocs given by each vector, b repetitions of the cross-validation

- 'vnbxx': venitian blinds, xx blocs; ex: 'vnb10' for 10 blocs

- 'blkxx' or 'jckxx':contiguous blocs = Jack knife, xx blocs; ex: 'jck8' for 8 blocs

var_split:

parameter to classify the variables into groups:

- an integer: number of random blocs

- a vector of dimension q attributing each variable to a bloc (numbers 1,2,...k for m blocs): blocs given by the vector

- 'vnbxx': venitian blinds, xx blocs; ex: 'vnb10' for 10 blocs

- 'blkxx' or 'jckxx':contiguous blocs = Jack knife, xx blocs; ex: 'jck8' for 8 blocs

(centred)

centering=1 (by default); no centering = 0

model.err:

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

model.ypredcv:

y predicted after cross validation for each of the latent variables

model.ypredcv.d is a matrix of dimensions (n x lv)

model.b:

the b coefficients or regression coefficients

model.b.d is a matrix of dimensions (q x lv)

model.x_mean, model.y_mean:

means of x and y; a vector (q x 1) and a scalar

model.center:

1 = centred, 0 = not centred

Exemples

[model]=spls(x,y,20,'vnb10','blk10')

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