Npls

Purpose

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

Synopsis

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

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

Please note that the recommended way to build and apply a N-PLS model from the command line is to use the Model Object. Please see this wiki page on building and applying models using the Model Object.

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: Standard Model Structure) 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,

• preprocessing: {[] []}, two element cell array containing preprocessing structures (see PREPROCESS) defining preprocessing to use on the x- and y-blocks (first and second elements respectively)

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

• blockdetails: [ 'compact' | {'standard'} | 'all' ] level of detail (predictions, raw residuals, and calibration data) included in the model.

• ‘Standard’ = the predictions and raw residuals for the X-block as well as the X-block itself are not stored in the model to reduce its size in memory. Specifically, these fields in the model object are left empty: 'model.pred{1}', 'model.detail.res{1}', 'model.detail.data{1}'.
• ‘Compact’ = for this function, 'compact' is like 'standard' but the residual limits in the model structure are also left empty (.model.detail.reslim.lim95, model.detail.reslim.lim99).
• 'All' = keep predictions, raw residuals for both X- & Y-blocks as well as the X- & Y-blocks themselves.

See Also

analysis, conload, datahat, explode, gram, modlrder, mpca, crossval, outerm, parafac, parafac2, pls, tld, unfoldm, EVRIModel_Objects