Difference between revisions of "Npls"

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===Description===
 
===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.
 
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:
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====INPUTS====
* x = X-block,
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* '''x''' = X-block,
* y = Y-block, and
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* '''y''' = Y-block, and
* ncomp = the number of factors to compute, or
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* '''ncomp''' = the number of factors to compute, or
* model = in prediction mode, this is a structure containing a NPLS model.
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* '''model''' = in prediction mode, this is a structure containing a NPLS model.
OPTIONAL INPUTS:
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====OPTIONAL INPUTS====
*'' options'' = discussed below.
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*'''''''' options'' = discussed below.
OUTPUT:
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====OUTPUTS====
* model = standard model structure (see: MODELSTRUCT) with the following fields:
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* '''model''' = standard model structure (see: MODELSTRUCT) with the following fields:
* modeltype: 'NPLS',
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* '''modeltype''': 'NPLS',
* datasource: structure array with information about input data,
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* '''datasource''': structure array with information about input data,
* date: date of creation,
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* '''date''': date of creation,
* time: time of creation,
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* '''time''': time of creation,
* info: additional model information,
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* '''info''': additional model information,
* reg: cell array with regression coefficients,
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* '''reg''': cell array with regression coefficients,
* loads: cell array with model loadings for each mode/dimension,
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* '''loads''': cell array with model loadings for each mode/dimension,
* core: cell array with the NPLS core,
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* '''core''': cell array with the NPLS core,
* pred: cell array with model predictions for each input data block,
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* '''pred''': cell array with model predictions for each input data block,
* tsqs: cell array with T<sup>2</sup> values for each mode,
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* '''tsqs''': cell array with T<sup>2</sup> values for each mode,
* ssqresiduals: cell array with sum of squares residuals for each mode,
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* '''ssqresiduals''': cell array with sum of squares residuals for each mode,
* description: cell array with text description of model, and
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* '''description''': cell array with text description of model, and
* detail: sub-structure with additional model details and results.
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* '''detail''': sub-structure with additional model details and results.
 
===Options===
 
===Options===
* ''options'' = options structure containing the fields:
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* '''''options''''' = options structure containing the fields:
* display: [ 'off' | {'on'} ], governs level of display to command window,
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* '''display''': [ 'off' | {'on'} ], governs level of display to command window,
* plots: [ 'none' | {'final'} ], governs level of plotting,
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* '''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
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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
* blockdetails: [ {'standard'} | 'all' ], level of detail included in the model for predictions and residuals.
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* '''blockdetails''': [ {'standard'} | 'all' ], level of detail included in the model for predictions and residuals.
 
===See Also===
 
===See Also===
 
[[datahat]], [[explode]], [[gram]], [[mpca]], [[outerm]], [[parafac]], [[pls]], [[tld]], [[unfoldm]]
 
[[datahat]], [[explode]], [[gram]], [[mpca]], [[outerm]], [[parafac]], [[pls]], [[tld]], [[unfoldm]]

Revision as of 20:57, 2 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)
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 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