# Difference between revisions of "Npls"

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===Purpose=== | ===Purpose=== | ||

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Multilinear-PLS (N-PLS) for true multi-way regression. | Multilinear-PLS (N-PLS) for true multi-way regression. | ||

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===Synopsis=== | ===Synopsis=== | ||

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:model = npls(x,y,ncomp,''options'') | :model = npls(x,y,ncomp,''options'') | ||

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:pred = npls(x,ncomp,model,''options'') | :pred = npls(x,ncomp,model,''options'') | ||

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:options = npls('options') | :options = npls('options') | ||

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===Description=== | ===Description=== | ||

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NPLS fits a multilinear PLS1 or PLS2 regression model to x and y [R. Bro, J. Chemom., 1996, 10(1), 47-62]. The NPLS function also can be used for calibration and prediction. | NPLS fits a multilinear PLS1 or PLS2 regression model to x and y [R. Bro, J. Chemom., 1996, 10(1), 47-62]. The NPLS function also can be used for calibration and prediction. | ||

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====INPUTS==== | ====INPUTS==== | ||

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* '''x''' = X-block, | * '''x''' = X-block, | ||

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* '''y''' = Y-block, and | * '''y''' = Y-block, and | ||

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* '''ncomp''' = the number of factors to compute, or | * '''ncomp''' = the number of factors to compute, or | ||

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* '''model''' = in prediction mode, this is a structure containing a NPLS model. | * '''model''' = in prediction mode, this is a structure containing a NPLS model. | ||

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====OPTIONAL INPUTS==== | ====OPTIONAL INPUTS==== | ||

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*'''''''' options'' = discussed below. | *'''''''' options'' = discussed below. | ||

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====OUTPUTS==== | ====OUTPUTS==== | ||

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* '''model''' = standard model structure (see: MODELSTRUCT) with the following fields: | * '''model''' = standard model structure (see: MODELSTRUCT) with the following fields: | ||

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* '''modeltype''': 'NPLS', | * '''modeltype''': 'NPLS', | ||

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* '''datasource''': structure array with information about input data, | * '''datasource''': structure array with information about input data, | ||

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* '''date''': date of creation, | * '''date''': date of creation, | ||

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* '''time''': time of creation, | * '''time''': time of creation, | ||

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* '''info''': additional model information, | * '''info''': additional model information, | ||

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* '''reg''': cell array with regression coefficients, | * '''reg''': cell array with regression coefficients, | ||

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* '''loads''': cell array with model loadings for each mode/dimension, | * '''loads''': cell array with model loadings for each mode/dimension, | ||

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* '''core''': cell array with the NPLS core, | * '''core''': cell array with the NPLS core, | ||

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* '''pred''': cell array with model predictions for each input data block, | * '''pred''': cell array with model predictions for each input data block, | ||

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* '''tsqs''': cell array with T<sup>2</sup> values for each mode, | * '''tsqs''': cell array with T<sup>2</sup> values for each mode, | ||

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* '''ssqresiduals''': cell array with sum of squares residuals for each mode, | * '''ssqresiduals''': cell array with sum of squares residuals for each mode, | ||

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* '''description''': cell array with text description of model, and | * '''description''': cell array with text description of model, and | ||

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* '''detail''': sub-structure with additional model details and results. | * '''detail''': sub-structure with additional model details and results. | ||

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===Options=== | ===Options=== | ||

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* '''''options''''' = options structure containing the fields: | * '''''options''''' = options structure containing the fields: | ||

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* '''display''': [ 'off' | {'on'} ], governs level of display to command window, | * '''display''': [ 'off' | {'on'} ], governs level of display to command window, | ||

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* '''plots''': [ 'none' | {'final'} ], governs level of plotting, | * '''plots''': [ 'none' | {'final'} ], governs level of plotting, | ||

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* '''outputregrescoef''': if this is set to 0 no regressions coefficients associated with the X-block directly are calculated (relevant for large arrays), and | * '''outputregrescoef''': if this is set to 0 no regressions coefficients associated with the X-block directly are calculated (relevant for large arrays), and | ||

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* '''blockdetails''': [ {'standard'} | 'all' ], level of detail included in the model for predictions and residuals. | * '''blockdetails''': [ {'standard'} | 'all' ], level of detail included in the model for predictions and residuals. | ||

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===See Also=== | ===See Also=== | ||

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[[datahat]], [[explode]], [[gram]], [[mpca]], [[outerm]], [[parafac]], [[pls]], [[tld]], [[unfoldm]] | [[datahat]], [[explode]], [[gram]], [[mpca]], [[outerm]], [[parafac]], [[pls]], [[tld]], [[unfoldm]] |

## Revision as of 15:26, 3 September 2008

## Contents

### Purpose

Multilinear-PLS (N-PLS) for true multi-way regression.

### Synopsis

- model = npls(x,y,ncomp,
*options*)

- pred = npls(x,ncomp,model,
*options*)

- options = npls('options')

### Description

NPLS fits a multilinear PLS1 or PLS2 regression model to x and y [R. Bro, J. Chemom., 1996, 10(1), 47-62]. The NPLS function also can be used for calibration and prediction.

#### INPUTS

**x**= X-block,

**y**= Y-block, and

**ncomp**= the number of factors to compute, or

**model**= in prediction mode, this is a structure containing a NPLS model.

#### OPTIONAL INPUTS

- '''
*options*= discussed below.

#### OUTPUTS

**model**= standard model structure (see: MODELSTRUCT) with the following fields:

**modeltype**: 'NPLS',

**datasource**: structure array with information about input data,

**date**: date of creation,

**time**: time of creation,

**info**: additional model information,

**reg**: cell array with regression coefficients,

**loads**: cell array with model loadings for each mode/dimension,

**core**: cell array with the NPLS core,

**pred**: cell array with model predictions for each input data block,

**tsqs**: cell array with T^{2}values for each mode,

**ssqresiduals**: cell array with sum of squares residuals for each mode,

**description**: cell array with text description of model, and

**detail**: sub-structure with additional model details and results.

### Options

= options structure containing the fields:**options**

**display**: [ 'off' | {'on'} ], governs level of display to command window,

**plots**: [ 'none' | {'final'} ], governs level of plotting,

**outputregrescoef**: if this is set to 0 no regressions coefficients associated with the X-block directly are calculated (relevant for large arrays), and

**blockdetails**: [ {'standard'} | 'all' ], level of detail included in the model for predictions and residuals.

### See Also

datahat, explode, gram, mpca, outerm, parafac, pls, tld, unfoldm