Genetic Algorithm (GA) for Variable Selection from High-Dimensional Data

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Documentation for package ‘gaselect’ version 1.0.11

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evaluate Evaluate the fitness of variable subsets
evaluate-method Evaluate the fitness of variable subsets
evaluatorFit Fit Evaluator
evaluatorLM LM Evaluator
evaluatorPLS PLS Evaluator
evaluatorUserFunction User Defined Evaluator
fitness Get the fitness of a variable subset
fitnessEvolution Get the evolution of the fitness
formatSegmentation Format the raw segmentation list returned from the C++ code into a usable list
formatSegmentation-method Format the raw segmentation list returned from the C++ code into a usable list
GenAlg Result of a genetic algorithm run
genAlg Genetic algorithm for variable subset selection
GenAlg-class Result of a genetic algorithm run
GenAlgControl Control class for the genetic algorithm
genAlgControl Set control arguments for the genetic algorithm
GenAlgControl-class Control class for the genetic algorithm
GenAlgEvaluator Evaluator Base Class
GenAlgEvaluator-class Evaluator Base Class
GenAlgFitEvaluator Fit Evaluator
GenAlgFitEvaluator-class Fit Evaluator
GenAlgLMEvaluator LM Evaluator
GenAlgLMEvaluator-class LM Evaluator
GenAlgPLSEvaluator PLS Evaluator
GenAlgPLSEvaluator-class PLS Evaluator
GenAlgUserEvaluator User Function Evaluator
GenAlgUserEvaluator-class User Function Evaluator
getEvalFun Get the evaluation function from a GenAlgUserEvaluator
getEvalFun-method Get the evaluation function from a GenAlgUserEvaluator
subsets Get the found variable subset(s)
toCControlList Transform the object to a list
toCControlList-method Transform the object to a list
trueFitnessVal Get the transformed fitness values
trueFitnessVal-method Get the transformed fitness values
validData Check if the data is valid for the evaluator
validData-method Check if the data is valid for the evaluator