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:
name: 'options', name indicating that this is an options structure,
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, tld, unfoldm
