Frpcr: Difference between revisions
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T.V. Karstang and R. Manne, "Optimized scaling: A novel approach to linear calibration with close data sets", Chemom. Intell. Lab. Syst., '''14''', 165-173 (1992). | T.V. Karstang and R. Manne, "Optimized scaling: A novel approach to linear calibration with close data sets", Chemom. Intell. Lab. Syst., '''14''', 165-173 (1992). | ||
===Options=== | ===Options=== | ||
* ''options'' = a structure with the following fields: | * '''''options''''' = a structure with the following fields: | ||
* pathvar: [ {0.5} ] standard deviation for random multiplicative scaling. A value of zero will disable the random sample scaling but may increase model sensitivity to scaling errors, | * '''pathvar''': [ {0.5} ] standard deviation for random multiplicative scaling. A value of zero will disable the random sample scaling but may increase model sensitivity to scaling errors, | ||
* useoffset: [ {'off'} | 'on' ] flag determining use of offset term in regression equations (may be necessary for mean-centered x-block), | * '''useoffset''': [ {'off'} | 'on' ] flag determining use of offset term in regression equations (may be necessary for mean-centered x-block), | ||
* display: [ {'off'} | 'on' ] governs level of display to command window, | * '''display''': [ {'off'} | 'on' ] governs level of display to command window, | ||
* plots: [ {'none'} | 'intermediate' | 'final' ] governs level of plotting, | * '''plots''': [ {'none'} | 'intermediate' | 'final' ] governs level of plotting, | ||
* preprocessing: {[ ] [ ]} cell of two preprocessing structures (see PREPROCESS) defining preprocessing for the x- and y-blocks. | * '''preprocessing''': {[ ] [ ]} cell of two preprocessing structures (see PREPROCESS) defining preprocessing for the x- and y-blocks. | ||
* algorithm: [ {'direct'} | 'empirical' ] governs solution algorithm. Direct solution is fastest and most stable. Only empirical will work on single-factor models when useoffset is 'on', and | * '''algorithm''': [ {'direct'} | 'empirical' ] governs solution algorithm. Direct solution is fastest and most stable. Only empirical will work on single-factor models when useoffset is 'on', and | ||
* blockdetails: [ 'compact' | {'standard'} | 'all' ] extent of predictions and raw residuals included in model. 'standard' only uses y-block, and 'all' uses x- and y-blocks. | * '''blockdetails''': [ 'compact' | {'standard'} | 'all' ] extent of predictions and raw residuals included in model. 'standard' only uses y-block, and 'all' uses x- and y-blocks. | ||
* confidencelimit: [ {'0.95'} ] Confidence level for Q and T2 limits. A value of zero (0) disables calculation of confidence limits. | * '''confidencelimit''': [ {'0.95'} ] Confidence level for Q and T2 limits. A value of zero (0) disables calculation of confidence limits. | ||
*In addition, there are several options relating to the algorithm. See FRPCRENGINE. | *'''In''' addition, there are several options relating to the algorithm. See FRPCRENGINE. | ||
The default options can be retreived using: options = frpcr('options');. | The default options can be retreived using: options = frpcr('options');. | ||
===See Also=== | ===See Also=== | ||
[[frpcrengine]], [[mscorr]], [[pcr]] | [[frpcrengine]], [[mscorr]], [[pcr]] |
Revision as of 20:56, 2 September 2008
Purpose
Full-ratio PCR calibration and prediction.
Synopsis
- model = frpcr(x,y,ncomp,options) %calibration
- pred = frpcr(x,model,options) %prediction
- valid = frpcr(x,y,model,options) %validation
- options = frpcr('options')
Description
FRPCR calculates a single full-ratio PCR model using the given number of components ncomp to predict y from measurements x. Random multiplicative scaling of each sample can be used to aid model stability. Full-Ratio PCR models are based on the simultaneous regression for both y-block prediction and scaling variations (such as those due to pathlength and collection efficiency variations). The resulting PCR model is insensitive to absolute scaling errors. NOTE: For best results, the x-block should not be mean-centered. Inputs are x the predictor block (2-way array or DataSet Object), y the predicted block (2-way array or DataSet Object), ncomp the number of components to to be calculated (positive integer scalar) and the optional options structure, options. The output of the function is a standard model structure model. In prediction and validation modes, the same model structure is used but predictions are provided in the model.detail.pred field. Although the full-ratio method uses a different method for determination of the regression vector, the fundamental idea is very similar to the optimized scaling 2 method as described in: T.V. Karstang and R. Manne, "Optimized scaling: A novel approach to linear calibration with close data sets", Chemom. Intell. Lab. Syst., 14, 165-173 (1992).
Options
- options = a structure with the following fields:
- pathvar: [ {0.5} ] standard deviation for random multiplicative scaling. A value of zero will disable the random sample scaling but may increase model sensitivity to scaling errors,
- useoffset: [ {'off'} | 'on' ] flag determining use of offset term in regression equations (may be necessary for mean-centered x-block),
- display: [ {'off'} | 'on' ] governs level of display to command window,
- plots: [ {'none'} | 'intermediate' | 'final' ] governs level of plotting,
- preprocessing: {[ ] [ ]} cell of two preprocessing structures (see PREPROCESS) defining preprocessing for the x- and y-blocks.
- algorithm: [ {'direct'} | 'empirical' ] governs solution algorithm. Direct solution is fastest and most stable. Only empirical will work on single-factor models when useoffset is 'on', and
- blockdetails: [ 'compact' | {'standard'} | 'all' ] extent of predictions and raw residuals included in model. 'standard' only uses y-block, and 'all' uses x- and y-blocks.
- confidencelimit: [ {'0.95'} ] Confidence level for Q and T2 limits. A value of zero (0) disables calculation of confidence limits.
- In addition, there are several options relating to the algorithm. See FRPCRENGINE.
The default options can be retreived using: options = frpcr('options');.