frpcr Documentation


PLS_Toolbox Documentation: frpcr	< fitpeaks	frpcrengine >

frpcr

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: [ {'standard'} | 'all' ] extent of predictions and raw residuals included in model. 'standard' only uses y-block, and 'all' uses x- and y-blocks.

The default options can be retreived using: options = frpcr('options');.