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imported>Scott (New page: ===Purpose=== Classical Least Squares regression for multivariate Y. ===Synopsis=== : model = cls(x,options); %identifies model (calibration step) : model = cls(x,y,options); ...) |
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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. | |||
Please note that the recommended way to build and apply a CLS model from the command line is to use the Model Object. Please see [[EVRIModel_Objects | this wiki page on building and applying models using the Model Object]]. | |||
===Description=== | ===Description=== | ||
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====Optional Inputs==== | ====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 | * '''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. | ||
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==== | ====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. | ||
* '''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. | ||
* confidencelimit: [{0.95}] Confidence level for Q and T2 limits. A value of zero (0) disables calculation of confidence limits. | : stepwise = stepwise least squares | ||
* blockdetails: [ 'compact' | {'standard'} | 'all' ] | : 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=== | ===See Also=== | ||
[[analysis]], [[pcr]], [[pls]], [[preprocess]] | [[analysis]], [[clsti]], [[pcr]], [[pls]], [[preprocess]], [[stepwise regrcls]], [[testrobustness]], [[EVRIModel_Objects]] | ||
Latest revision as of 10:57, 8 December 2023
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.
Please note that the recommended way to build and apply a CLS model from the command line is to use the Model Object. Please see this wiki page on building and applying models using the Model Object.
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, clsti, pcr, pls, preprocess, stepwise regrcls, testrobustness, EVRIModel_Objects