Mlrengine: Difference between revisions
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===Options === | ===Options === | ||
'''options''' = a structure array with the following fields. | |||
* ''' | * '''algorithm''': [ {'leastsquares'} | 'ridge' | 'ridge_hkb' | 'optimized_ridge' | 'optimized_lasso' | 'elasticnet' ] Governs the level of regularization used when calculating the regression vector. | ||
* '''condmax''': [{[ ]}] Value for maximum condition number. Default value = [] leads to MLR calculation at full rank. Any value > 0 leads to truncation of factors based upon SVD until the condition number is less than the specified value. Used only for algorithm 'leastsquares'. | |||
* '''ridge''': [ {1} ] Scalar value for ridge parameter for algorithm 'ridge'. | |||
* '''optimized_ridge''': [{1.0000e-05}] Scalar value for ridge parameter θ for algorithms 'optimized_ridge' or 'elasticnet'. | |||
* '''optimized_lasso''': [{1.0000e-05}] Scalar value for ridge parameter θ for algorithms 'optimized_lasso' or 'elasticnet'. | |||
===See Also=== | ===See Also=== | ||
[[analysis]], [[pcr]], [[pls]] | [[mlr]], [[analysis]], [[pcr]], [[pls]] | ||
Latest revision as of 11:05, 23 August 2022
Purpose
Multiple Linear Regression computational engine.
Synopsis
- reg = mlrengine(x,y,options)
Description
Inputs are an x-block x, y-block y and optional options structure.
Output is the matrix of regression vectors reg.
Options
options = a structure array with the following fields.
- algorithm: [ {'leastsquares'} | 'ridge' | 'ridge_hkb' | 'optimized_ridge' | 'optimized_lasso' | 'elasticnet' ] Governs the level of regularization used when calculating the regression vector.
- condmax: [{[ ]}] Value for maximum condition number. Default value = [] leads to MLR calculation at full rank. Any value > 0 leads to truncation of factors based upon SVD until the condition number is less than the specified value. Used only for algorithm 'leastsquares'.
- ridge: [ {1} ] Scalar value for ridge parameter for algorithm 'ridge'.
- optimized_ridge: [{1.0000e-05}] Scalar value for ridge parameter θ for algorithms 'optimized_ridge' or 'elasticnet'.
- optimized_lasso: [{1.0000e-05}] Scalar value for ridge parameter θ for algorithms 'optimized_lasso' or 'elasticnet'.