Cls: Difference between revisions

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====Outputs====
====Outputs====
* '''model''' = standard model structure containing the CLS model (See MODELSTRUCT).
* '''model''' = standard model structure containing the CLS model (See [[Standard Model Structure]]).
* '''pred''' = structure array with predictions.
* '''pred''' = structure array with predictions.
* '''valid''' = structure array with predictions.
* '''valid''' = structure array with predictions.

Revision as of 10:51, 21 December 2011

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

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.
  • 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.

See Also

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