From Eigenvector Research Documentation Wiki
Revision as of 17:21, 7 October 2008 by imported>Scott (See Also)
Jump to navigation Jump to search


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


model = npls(x,y,ncomp,options)
pred = npls(x,ncomp,model,options)


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.


  • 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.


  • 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 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, modlrder, mpca, ncrossval, outerm, parafac, pls, tld, unfoldm