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vodka

the Vodka regression

Calling sequence

model=vodka(x,y,split,lv,r,centred)

Arguments

x and y :

the 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

r :

a parameter for Vodka; a vector (q x 1) or an integer among {0,1,2}:

  • r=0 --> the mean of the columns of x
  • r=1 --> x'.y that is standard PLSR
  • r=2 --> x'.y2 with y2 = square of y
(centred):

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

model.rmsec:

the root mean square error of calibration

model.rmsec.d is a vector of dimension (lv x 1)

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.b:

the b coeficients or regression coefficients

model.b.d is a matrix (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

model.x_mean, model.y_mean:

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

rmsec:

rmsec=model.rmsec.d

rmsecv:

rmsecv=model.rmsecv.d

b:

b=model.b.d

ypredcv:

ypredcv=model.ypredcv.d

Examples

[model]=vodka(x,y,50,20,1)
[model]=vodka(x,y,50,20,1,0)
[model]=vodka(x,y,50,20,mean(x,'r'),0)

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