Simpls: Difference between revisions
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===Description=== | ===Description=== | ||
SIMPLS performs PLS regression using SIMPLS algorithm. | SIMPLS performs PLS regression using SIMPLS algorithm. | ||
INPUTS | ====INPUTS==== | ||
* x = X-block (predictor block) class "double" or "dataset", and | * '''x''' = X-block (predictor block) class "double" or "dataset", and | ||
* y = Y-block (predicted block) class "double" or "dataset". | * '''y''' = Y-block (predicted block) class "double" or "dataset". | ||
OPIONAL INPUTS: | OPIONAL INPUTS: | ||
* ''ncomp'' = integer, number of latent variables to use in {default = rank of X-block}, and | * '''''ncomp''''' = integer, number of latent variables to use in {default = rank of X-block}, and | ||
* ''options'' = a structure array discussed below. | * '''''options''''' = a structure array discussed below. | ||
OUPUTS: | OUPUTS: | ||
* reg = matrix of regression vectors, | * '''reg''' = matrix of regression vectors, | ||
* ssq = the sum of squares captured (ssq), | * '''ssq''' = the sum of squares captured (ssq), | ||
* xlds = X-block loadings, | * '''xlds''' = X-block loadings, | ||
* ylds = Y-block loadings, | * '''ylds''' = Y-block loadings, | ||
* wts = X-block weights, | * '''wts''' = X-block weights, | ||
* xscrs = X-block scores, | * '''xscrs''' = X-block scores, | ||
* yscrs = Y-block scores, and | * '''yscrs''' = Y-block scores, and | ||
* basis = the basis of X-block loadings. | * '''basis''' = the basis of X-block loadings. | ||
Note: The regression matrices are ordered in reg such that each ''Ny'' (number of Y-block variables) rows correspond to the regression matrix for that particular number of latent variables. | Note: The regression matrices are ordered in reg such that each ''Ny'' (number of Y-block variables) rows correspond to the regression matrix for that particular number of latent variables. | ||
NOTE: in previous versions of SIMPLS, the X-block scores were unit length and the X-block loadings contained the variance. As of Version 3.0, this algorithm now uses standard convention in which the X-block scores contain the variance. | NOTE: in previous versions of SIMPLS, the X-block scores were unit length and the X-block loadings contained the variance. As of Version 3.0, this algorithm now uses standard convention in which the X-block scores contain the variance. | ||
===Options=== | ===Options=== | ||
* ''options'' = a structure array with the following fields: | * '''''options''''' = a structure array with the following fields: | ||
* display: [ {'on'} | 'off' ], governs level of display, and | * '''display''': [ {'on'} | 'off' ], governs level of display, and | ||
* ranktest: [ 'none' | 'data' | 'scores' | {'auto'} ], governs type of rank test to perform. | * '''ranktest''': [ 'none' | 'data' | 'scores' | {'auto'} ], governs type of rank test to perform. | ||
* 'data' = single test on X-block (faster with smaller data blocks and more components), | * ''''data'''' = single test on X-block (faster with smaller data blocks and more components), | ||
* 'scores' = test during regression on scores matrix (faster with larger data matricies), | * ''''scores'''' = test during regression on scores matrix (faster with larger data matricies), | ||
* 'auto' = automatic selection, or | * ''''auto'''' = automatic selection, or | ||
* 'none' = assumes X-block has sufficient rank. | * ''''none'''' = assumes X-block has sufficient rank. | ||
The default options can be retreived using: options = simpls('options');. | The default options can be retreived using: options = simpls('options');. | ||
===See Also=== | ===See Also=== | ||
[[crossval]], [[modelstruct]], [[pcr]], [[plsnipal]], [[preprocess]], [[analysis]] | [[crossval]], [[modelstruct]], [[pcr]], [[plsnipal]], [[preprocess]], [[analysis]] | ||
Revision as of 19:57, 2 September 2008
Purpose
Partial Least Squares regression using the SIMPLS algorithm.
Synopsis
- [reg,ssq,xlds,ylds,wts,xscrs,yscrs,basis] = simpls(x,y,ncomp,options)
- options = simpls('options');.
Description
SIMPLS performs PLS regression using SIMPLS algorithm.
INPUTS
- x = X-block (predictor block) class "double" or "dataset", and
- y = Y-block (predicted block) class "double" or "dataset".
OPIONAL INPUTS:
- ncomp = integer, number of latent variables to use in {default = rank of X-block}, and
- options = a structure array discussed below.
OUPUTS:
- reg = matrix of regression vectors,
- ssq = the sum of squares captured (ssq),
- xlds = X-block loadings,
- ylds = Y-block loadings,
- wts = X-block weights,
- xscrs = X-block scores,
- yscrs = Y-block scores, and
- basis = the basis of X-block loadings.
Note: The regression matrices are ordered in reg such that each Ny (number of Y-block variables) rows correspond to the regression matrix for that particular number of latent variables. NOTE: in previous versions of SIMPLS, the X-block scores were unit length and the X-block loadings contained the variance. As of Version 3.0, this algorithm now uses standard convention in which the X-block scores contain the variance.
Options
- options = a structure array with the following fields:
- display: [ {'on'} | 'off' ], governs level of display, and
- ranktest: [ 'none' | 'data' | 'scores' | {'auto'} ], governs type of rank test to perform.
- 'data' = single test on X-block (faster with smaller data blocks and more components),
- 'scores' = test during regression on scores matrix (faster with larger data matricies),
- 'auto' = automatic selection, or
- 'none' = assumes X-block has sufficient rank.
The default options can be retreived using: options = simpls('options');.