# Cls

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## Contents

### 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, pcr, pls, preprocess, stepwise regrcls, testrobustness, EVRIModel_Objects