Npls: Difference between revisions
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imported>Jeremy |
imported>Scott |
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===See Also=== | ===See Also=== | ||
[[datahat]], [[explode]], [[gram]], [[modlrder]], [[mpca]], [[crossval]], [[outerm]], [[parafac]], [[pls]], [[tld]], [[unfoldm]] | [[conload]], [[datahat]], [[explode]], [[gram]], [[modlrder]], [[mpca]], [[crossval]], [[outerm]], [[parafac]], [[pls]], [[tld]], [[unfoldm]] |
Revision as of 10:26, 21 September 2011
Purpose
Multilinear-PLS (N-PLS) for true multi-way regression.
Synopsis
- model = npls(x,y,ncomp,options)
- pred = npls(x,ncomp,model,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,
- 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: [ {'standard'} | 'all' ], level of detail included in the model for predictions and residuals.
See Also
conload, datahat, explode, gram, modlrder, mpca, crossval, outerm, parafac, pls, tld, unfoldm