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fact >> (X->y) Linear calibrations > mlr

mlr

multiple linear regression (MLR)

Calling sequence

model=mlr(x,y,split,(centred))

Arguments

x, y:

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

warning: when x is not square and of full rank, the MLR is computed with the Moore-Penrose pseudo-inverse of xx'

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

(centred):

centred=1 (by default); not centred=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:

the predicted values of y after cross-validation

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

model.b:

the b-vector or vector of the regression coefficients

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

model.scores:

the scores = x

model.loadings:

the loadings = the identity matrix

model.x_mean, model.y_mean:

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

model.center:

centred=1; not centred=0

rmsec:

rmsec=model.rmsec.d

rmsecv:

rmsecv=model.rmsecv.d

b:

b=model.b.d

ypredcv:

ypredcv=model.ypredcv.d

Examples

[model]=mlr(x,y,50)
[model]=mlr(x,y,50,0)

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