Npls: Difference between revisions
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:model = npls(x,y,ncomp,''options'') | :model = npls(x,y,ncomp,''options'') | ||
:pred = npls(x,ncomp,model,''options'') | :pred = npls(x,ncomp,model,''options'') | ||
===Description=== | ===Description=== | ||
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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. | 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, | * '''x''' = X-block, | ||
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* '''model''' = in prediction mode, this is a structure containing a NPLS model. | * '''model''' = in prediction mode, this is a structure containing a NPLS model. | ||
==== | ====Optional Inputs==== | ||
*'''''''' options'' = discussed below. | *'''''''' options'' = discussed below. | ||
==== | ====Outputs==== | ||
* '''model''' = standard model structure (see: MODELSTRUCT) with the following fields: | * '''model''' = standard model structure (see: MODELSTRUCT) with the following fields: |
Revision as of 17:34, 3 September 2008
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,
- 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