Stepwise regrcls and File:Select Shortcuts to Show dialogbox.png: Difference between pages

From Eigenvector Research Documentation Wiki
(Difference between pages)
Jump to navigation Jump to search
imported>Donal
(Created page with "===Purpose=== STEPWISE_REGRCLS Step-wise regression for CLS models. ===Synopsis=== :[c,ikeep,res] = stepwise_regrcls(x,targspec,options); :options = stepwise_regrcls('options'...")
 
(Maintenance script uploaded File:Select Shortcuts to Show dialogbox.png)
 
Line 1: Line 1:
===Purpose===
STEPWISE_REGRCLS Step-wise regression for CLS models.


===Synopsis===
:[c,ikeep,res] = stepwise_regrcls(x,targspec,options);
:options  = stepwise_regrcls('options');
===Description===
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:
    x(i,:) = c(i,:)*targspec(ikeep{i},:)
where c(i,:) can be determined using non-negative least-squares
[see optional input (options)].
===Inputs===
* '''x''' = MxN matrix of measured spectra (each row corresponds to a measured spectrum).
* '''targspec ''' = KxN matrix of target (candidate) spectra.
===Outputs===
* '''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).
* '''ikeep ''' = Mx1 cell array of indices, each cell corresponds to a row of input (x) and includes the indices of retained target spectra.
* '''res ''' = Mx1 vector of mean sum-squared-residuals.
===Options===
''options'' =  an optional options structure containing one or more of the following fields:
* '''display''': [ 'off' | {'on'} ], governs level of display to command window,
* '''automate''': [ {'yes'} | 'no' ], automate the algorithm?
: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.
:automate = 'no', requires interactive user input for each of the M spectra in (x).
        fstat: 0.95, probability level the F test that determines the
                significance of fit improvement.
          ccon: [ 'none' | {'nnls'} ], uses non-negativity on concentrations,
                in the concentration estimates.
          ccov: [], sqrt inverse noise/clutter covariance matrix,
                e.g. if Xc is a matrix of measured clutter spectra then
                ccov = inv(sqrt(cov(Xc))) [see COV_CV].
            p: KpxN matrix of spectra that are always included in the
                model.
          scls: [1:N],  % 1xN spectra scale axis {default = 1:N}.

Latest revision as of 11:44, 1 August 2019