Splitcaltest: Difference between revisions

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===Description===
===Description===


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. A randomization is used in the splitting process so no assumption about the data acquisition order is necessary.
The split is based on the scores from the input model. If a matrix or DataSet is passed in place of a model, it is assumed to contain the scores for the data. A randomization is used in the splitting process so no assumption about the data acquisition order is necessary.


====Inputs====
====Inputs====

Revision as of 09:55, 10 January 2013

Purpose

Splits data into calibration and test sets.

Synopsis

z = splitcaltest(model,options); %identifies model (calibration step)
Also available in the Analysis interface via the data context menu

Description

The split is based on the scores from the input model. If a matrix or DataSet is passed in place of a model, it is assumed to contain the scores for the data. A randomization is used in the splitting process so no assumption about the data acquisition order is necessary.

Inputs

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

Outputs

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

Options

  • options = structure array with the following fields :
  • plots: [ 'none' | {'final'} ] governs level of plotting
  • algorithm: [ {'onion'} ]
  • 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.