Splitcaltest: Difference between revisions
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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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* '''options''' = structure array with the following fields : | * '''options''' = structure array with the following fields : | ||
* '''plots''': [ 'none' | {'final'} ] governs level of plotting | * '''plots''': [ 'none' | {'final'} ] governs level of plotting | ||
* '''algorithm''': [ {'onion'} ] | * '''algorithm''': [ {'onion'} ] | ||
* '''nonion''': [{3}] the number of 'external layers' | * '''nonion''': [ {3} ] the number of 'external layers' | ||
* '''fraction''': [{0.66}] fraction of data to be set as calibrations samples. | * '''fraction''': [ {0.66} ] fraction of data to be set as calibrations samples. | ||
===See Also=== | ===See Also=== | ||
[[crossval]], [[pca]], [[pcr]], [[preprocess]]. | [[crossval]], [[pca]], [[pcr]], [[preprocess]]. | ||
Revision as of 10:45, 4 October 2012
Purpose
Splits randomly ordered data into calibration and test sets.
Synopsis
- z = splitcaltest(model,options); %identifies model (calibration step)
Description
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.
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.