Sratio

Purpose

Calculates selectivity ratio for a given regression model.

Synopsis

sr = sratio(x,model,options)

Description

Inputs are the calibration data (x) and the regression model calculated from those data (model). Output is the selectivity ratio. The larger the selectivity ratio, the more useful the given variables are for the prediction. Variables with lower selectivity ratio may be excluded without degrading the performance of the model (and exclusion may help performance).

Inputs

• x = calibration data (optionally: preprocessed calibration data, see options.preprocessed flag).
• model = regression model (PLS, PCR, MLR, etc).

Outputs

• sr = selectivity ratio for each variable included in the model.

Options

options = a structure array with the following fields:

• preprocessed : [{false}| true ] When true, treat x as if it is already preprocessed and ignore preprocessing stored in model. (Used when preprocessing has already been performed and can be skipped.)