Cls: Difference between revisions
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* '''plots''': [ 'none' | {'final'} ] governs level of plotting. | * '''plots''': [ 'none' | {'final'} ] governs level of plotting. | ||
* '''preprocessing''': { [] [] } preprocessing structure (see PREPROCESS). | * '''preprocessing''': { [] [] } preprocessing structure (see PREPROCESS). | ||
* '''algorithm''': [ {'ls'} | 'nnls' | 'snnls' | 'cnnls' ] Specifies the regression algorithm. :Options are: | * '''algorithm''': [ {'ls'} | 'nnls' | 'snnls' | 'cnnls' | 'stepwise' | 'stepwisennls' ] Specifies the regression algorithm. | ||
:Options are: | |||
: ls = a standard least-squares fit. | : ls = a standard least-squares fit. | ||
: snnls = non-negative least squares on spectra (S) only. | : snnls = non-negative least squares on spectra (S) only. | ||
: cnnls = non-negative least squares on concentrations (C) only. | : cnnls = non-negative least squares on concentrations (C) only. | ||
: nnls = non-negative least squares fit on both C and S. | : 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. | * '''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. | * '''blockdetails''': [ 'compact' | {'standard'} | 'all' ] Extent of predictions and raw residuals included in model. 'standard' = only y-block, 'all' x and y blocks. |
Revision as of 17:29, 1 March 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 MODELSTRUCT).
- 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