Figmerit: Difference between revisions
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===Purpose=== | ===Purpose=== | ||
Analytical figures of merit for multivariate calibration. | Analytical figures of merit for multivariate calibration. | ||
===Synopsis=== | ===Synopsis=== | ||
:[nas,nnas,sens,sel] = figmerit(x,y,b); | :[nas,nnas,sens,sel] = figmerit(x,y,b); | ||
===Description=== | ===Description=== | ||
Calculates analytical figures of merit for PLS and PCR standard model structures. Inputs are the preprocessed (usually centered and scaled) spectral data x, the preprocessed analyte data y, and the regression vector, b. Note that for standard PLS and PCR structures b = model.reg. | Calculates analytical figures of merit for PLS and PCR standard model structures. Inputs are the preprocessed (usually centered and scaled) spectral data x, the preprocessed analyte data y, and the regression vector, b. Note that for standard PLS and PCR structures b = model.reg. | ||
The outputs are the matrix of net analyte signals nas for each row of x, the norm of the net analyte signal for each row nnas (this is corrected to include the sign of the prediction), the matrix of sensitivities for each sample sens, and the vector of selectivities for each sample sel (sel is always non-negative). | The outputs are the matrix of net analyte signals nas for each row of x, the norm of the net analyte signal for each row nnas (this is corrected to include the sign of the prediction), the matrix of sensitivities for each sample sens, and the vector of selectivities for each sample sel (sel is always non-negative). | ||
Note that the "noise-filtered" estimate present in previous versions is no longer used because an improved method for calculating the net analyte vector makes it redundant | Note that the "noise-filtered" estimate present in previous versions is no longer used because an improved method for calculating the net analyte vector makes it redundant | ||
===Examples=== | ===Examples=== | ||
Given the 7 LV PLS model: | Given the 7 LV PLS model: | ||
:modl = pls(x,y,7); | :modl = pls(x,y,7); | ||
:Rhat = modl.loads{1,1}\*modl.loads{2,1}'; | :Rhat = modl.loads{1,1}\*modl.loads{2,1}'; | ||
:[nas,nnas,sens,sel,nfnas] = figmerit(x,y,Rhat); | :[nas,nnas,sens,sel,nfnas] = figmerit(x,y,Rhat); | ||
Given the 5 PC PCR model: | Given the 5 PC PCR model: | ||
:modl = pcr(auto(x),auto(y),5); | :modl = pcr(auto(x),auto(y),5); | ||
:Rhat = modl.loads{1,1}\*modl.loads{2,1}'; | :Rhat = modl.loads{1,1}\*modl.loads{2,1}'; | ||
:[nas,nnas,sens,sel,nfnas] = figmerit(auto(x),auto(y),Rhat); | :[nas,nnas,sens,sel,nfnas] = figmerit(auto(x),auto(y),Rhat); | ||
===See Also=== | ===See Also=== | ||
[[pcr]], [[pls]] | [[pcr]], [[pls]] | ||
Revision as of 15:25, 3 September 2008
Purpose
Analytical figures of merit for multivariate calibration.
Synopsis
- [nas,nnas,sens,sel] = figmerit(x,y,b);
Description
Calculates analytical figures of merit for PLS and PCR standard model structures. Inputs are the preprocessed (usually centered and scaled) spectral data x, the preprocessed analyte data y, and the regression vector, b. Note that for standard PLS and PCR structures b = model.reg.
The outputs are the matrix of net analyte signals nas for each row of x, the norm of the net analyte signal for each row nnas (this is corrected to include the sign of the prediction), the matrix of sensitivities for each sample sens, and the vector of selectivities for each sample sel (sel is always non-negative).
Note that the "noise-filtered" estimate present in previous versions is no longer used because an improved method for calculating the net analyte vector makes it redundant
Examples
Given the 7 LV PLS model:
- modl = pls(x,y,7);
- Rhat = modl.loads{1,1}\*modl.loads{2,1}';
- [nas,nnas,sens,sel,nfnas] = figmerit(x,y,Rhat);
Given the 5 PC PCR model:
- modl = pcr(auto(x),auto(y),5);
- Rhat = modl.loads{1,1}\*modl.loads{2,1}';
- [nas,nnas,sens,sel,nfnas] = figmerit(auto(x),auto(y),Rhat);