Splitcaltest

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'} ] 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.