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pls

partial least squares regression, projection to latent structure regression or PLSR computed with the standard algorithm

Calling sequence

[model, rmsec, rmsecv, b,ypredcv]=pls(x,y,split,lv,(centred))

Arguments

x and y :

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

split:

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

lv :

number of latent variables

(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.scores:

the scores of the observations onto the loadings

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

model.loadings:

the loadings

model.loadings.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

rmsec:

rmsec=model.rmsec.d

rmsecv:

rmsecv=model.rmsecv.d

b:

b=model.b.d

ypredcv:

ypredcv=model.ypredcv.d

x_ref:

model.x_ref

y_ref:

model.y_ref

Examples

[model]=pls(x,y,50,20)
[model]=pls(x,y,50,20,0)

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