Npls

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.

OUTPUT:

• 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