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Revision as of 10:43, 4 October 2012 by imported>Donal (Created page with "===Purpose=== Splits randomly ordered data into calibration and test sets. ===Synopsis=== :z = splitcaltest(model,options); %identifies model (calibration step) ===Descripti...")
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Splits randomly ordered data into calibration and test sets.


z = splitcaltest(model,options); %identifies model (calibration step)


The calibration and test data are split up under the assumption that the data were acquired in a random sequence. The split is based on the scores from the input model. If a matrix or DataSet are passed in place of a model, it is assumed to contain the scores for the data.


  • model = standard model structure from a factor-based model OR a double or DataSet object containing the scores to analyze.


  • z = a structure containing the class and classlookup table.


  • options = structure array with the following fields :
  • plots: [ 'none' | {'final'} ] governs level of plotting.
  • algorithm: [ {'onion'} ] A cell containing a preprocessing structure or keyword (see PREPROCESS). Use {'autoscale'} to perform autoscaling on reference and test data.
  • nonion: [{3}] the number of 'external layers'
  • fraction: [{0.66}] fraction of data to be set as calibrations samples.

See Also

crossval, pca, pcr, preprocess.