multiple linear regression (MLR)
model=mlr(x,y,split,(centred))
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'
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=1 (by default); not centred=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
the predicted values of y after cross-validation
model.ypredcv.d is a matrix of dimensions (n x q)
the b-vector or vector of the regression coefficients
model.b.d is a matrix of dimensions (q x q)
the scores = x
the loadings = the identity matrix
means of x and y , a vector (q x 1) and a scalar
centred=1; not centred=0
rmsec=model.rmsec.d
rmsecv=model.rmsecv.d
b=model.b.d
ypredcv=model.ypredcv.d