imported>Donal |
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| ===Purpose===
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| STEPWISE_REGRCLS Step-wise regression for CLS models.
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| ===Synopsis===
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| :[c,ikeep,res] = stepwise_regrcls(x,targspec,options);
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| :options = stepwise_regrcls('options');
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|
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| ===Description===
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| For a given set of measured spectra (x), STEPWISE_REGRCLS finds the subset of target spectra (targspec) that best fit each measured spectrum in (x). This can be used for classification i.e. an analyte identification algorithm. The model is:
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| x(i,:) = c(i,:)*targspec(ikeep{i},:)
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| where c(i,:) can be determined using non-negative least-squares
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| [see optional input (options)].
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|
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| ===Inputs===
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| * '''x''' = MxN matrix of measured spectra (each row corresponds to a measured spectrum).
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| * '''targspec ''' = KxN matrix of target (candidate) spectra.
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| ===Outputs===
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| * '''c''' = MxK matrix of concentrations / contributions, c is non-zero only if a corresponding target spectrum is retained. If (options.p) is not empty, then (c) is Mx(Kp+K) where the first Kp rows correspond to the spectra in (options.p).
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| * '''ikeep ''' = Mx1 cell array of indices, each cell corresponds to a row of input (x) and includes the indices of retained target spectra.
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| * '''res ''' = Mx1 vector of mean sum-squared-residuals.
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|
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| ===Options===
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| ''options'' = an optional options structure containing one or more of the following fields:
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| * '''display''': [ 'off' | {'on'} ], governs level of display to command window,
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| * '''automate''': [ {'yes'} | 'no' ], automate the algorithm?
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| :automate = 'yes', makes no plots, and the step-wise regression stops when the fit improvement is not sigificant in an F Test at the probability level given in options.fstat.
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| :automate = 'no', requires interactive user input for each of the M spectra in (x).
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| fstat: 0.95, probability level the F test that determines the
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| significance of fit improvement.
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| ccon: [ 'none' | {'nnls'} ], uses non-negativity on concentrations,
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| in the concentration estimates.
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| ccov: [], sqrt inverse noise/clutter covariance matrix,
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| e.g. if Xc is a matrix of measured clutter spectra then
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| ccov = inv(sqrt(cov(Xc))) [see COV_CV].
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| p: KpxN matrix of spectra that are always included in the
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| model.
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| scls: [1:N], % 1xN spectra scale axis {default = 1:N}.
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