Difference between revisions of "Npls"

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:model = npls(x,y,ncomp,''options'')
 
:model = npls(x,y,ncomp,''options'')
 
 
:pred  = npls(x,ncomp,model,''options'')
 
:pred  = npls(x,ncomp,model,''options'')
 
:options = npls('options')
 
  
 
===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.
  
====INPUTS====
+
====Inputs====
  
 
* '''x''' = X-block,
 
* '''x''' = X-block,
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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.
  
====OPTIONAL INPUTS====
+
====Optional Inputs====
  
 
*'''''''' options'' = discussed below.
 
*'''''''' options'' = discussed below.
  
====OUTPUTS====
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====Outputs====
  
 
* '''model''' = standard model structure (see: MODELSTRUCT) with the following fields:
 
* '''model''' = standard model structure (see: MODELSTRUCT) with the following fields:

Revision as of 17:34, 3 September 2008

Purpose

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

Synopsis

model = npls(x,y,ncomp,options)
pred = npls(x,ncomp,model,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 T2 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 = options structure containing the fields:
  • 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