Cls: Difference between revisions
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===Synopsis=== | ===Synopsis=== | ||
: model = cls(x,options); %identifies model (calibration step) | : model = cls(x,options); %identifies model (calibration step) | ||
: model = cls(x,y,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 | : 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 | : 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=== | ===Description=== | ||
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====Outputs==== | ====Outputs==== | ||
* '''model''' = standard model structure containing the CLS model (See | * '''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. | ||
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* '''plots''': [ {'none'} | 'final' ] governs plotting of results. | * '''plots''': [ {'none'} | 'final' ] governs plotting of results. | ||
* '''display''': [ 'off' | {'on'} ] governs level of display to command window. | * '''display''': [ 'off' | {'on'} ] governs level of display to command window. | ||
* '''plots''': [ 'none' | {'final'} ] governs level of plotting. | * '''plots''': [ 'none' | {'final'} ] governs level of plotting. | ||
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* '''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' ] | * '''blockdetails''': [ 'compact' | {'standard'} | 'all' ] level of detail (predictions, raw residuals, and calibration data) included in the model. | ||
:* ‘Standard’ = 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}'. | |||
:* ‘Compact’ = for this function, 'compact' is identical to 'standard'. | |||
:* 'All' = keep predictions, raw residuals for both X- & Y-blocks as well as the X- & Y-blocks themselves. | |||
===See Also=== | ===See Also=== | ||
[[analysis]], [[pcr]], [[pls]], [[preprocess]] | [[analysis]], [[pcr]], [[pls]], [[preprocess]], [[stepwise regrcls]], [[testrobustness]] | ||
Latest revision as of 11:02, 28 July 2017
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.
- ‘Standard’ = 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}'.
- ‘Compact’ = for this function, 'compact' is identical to 'standard'.
- '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