Cls

Classical Least Squares regression for multivariate Y.

model = cls(x,options); %identifies model (calibration step)

model = cls(x,y,options); %identifies model (calibration step)

pred = cls(x,model,options); %makes predictions with a new X-block

valid = cls(x,y,model,options); %makes predictions with new X- & Y-block

CLS identifies models of the form y = Xb + e.

y = Y-block: predicted block (2-way array or DataSet Object). The number of columns of y indicates the number of components in the model (each row specifies the mixture present in the given sample). If y is omitted, x is assumed to be a set of pure component responses (e.g. spectra) defining the model itself.

model = standard model structure containing the CLS model (See MODELSTRUCT).
pred = structure array with predictions.
valid = structure array with predictions.

options = a structure array with the following fields:

plots: [ {'none'} | 'final' ] governs plotting of results.
order: positive integer for polynomial order {default = 1}.
display: [ 'off' | {'on'} ] governs level of display to command window.
plots: [ 'none' | {'final'} ] governs level of plotting.
preprocessing: { [] [] } preprocessing structure (see PREPROCESS).
algorithm: [ {'ls'} | 'nnls' | 'snnls' | 'cnnls' | 'stepwise' | 'stepwisennls' ] Specifies the regression algorithm.

Options are:

ls = a standard least-squares fit.

snnls = non-negative least squares on spectra (S) only.

cnnls = non-negative least squares on concentrations (C) only.

nnls = non-negative least squares fit on both C and S.

stepwise = stepwise least squares

stepwisennls = stepwise non-negative least squares

confidencelimit: [{0.95}] Confidence level for Q and T2 limits. A value of zero (0) disables calculation of confidence limits.
blockdetails: [ 'compact' | {'standard'} | 'all' ] Extent of predictions and raw residuals included in model. 'standard' = only y-block, 'all' x and y blocks.

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