Cls

Purpose

Classical Least Squares regression for multivariate Y.

Synopsis

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  % Launches the Analysis window with CLS as the selected method.

Description

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

Inputs

• x = X-block: predictor block (2-way array or DataSet Object).

Optional Inputs

• 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.

Outputs

• model = standard model structure containing the CLS model (See Standard Model Structure).
• pred = structure array with predictions.
• valid = structure array with predictions.

Options

options = a structure array with the following fields:

• plots: [ {'none'} | 'final' ] governs plotting of results.
• 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' ] level of detail (predictions, raw residuals, and calibration data) included in the model.

• ‘Compact’ - the predictions and raw residuals for the X-block as well as the X-block itself are not stored in the model to reduce its size in memory. Specifically, these fields in the model object are left empty: 'model.pred{1}', 'model.detail.res{1}', 'model.detail.data{1}'.:* ‘Standard’ - use the default blockdetails setting for this model: ‘Compact’.
• ‘Standard’ - use the default blockdetails setting for this model: ‘Compact’.
• 'All' - keep predictions, raw residuals for both X- & Y-blocks as well as the X- & Y-blocks themselves.

See Also

analysis, pcr, pls, preprocess, stepwise regrcls, testrobustness