GenAlgFitEvaluator-constructor: Fit Evaluator

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

Fit Evaluator

Description

Creates the object that controls the evaluation step in the genetic algorithm

Usage

evaluatorFit(
  numSegments = 7L,
  statistic = c("BIC", "AIC", "adjusted.r.squared", "r.squared"),
  numThreads = NULL,
  maxNComp = NULL,
  sdfact = 1
)

Arguments

numSegments	The number of CV segments used to estimate the optimal number of PLS components (between 2 and 2^16).
statistic	The statistic used to evaluate the fitness (BIC, AIC, adjusted R^2, or R^2).
numThreads	The maximum number of threads the algorithm is allowed to spawn (a value less than 1 or NULL means no threads).
maxNComp	The maximum number of components the PLS models should consider (if not specified, the number of components is not constrained)
sdfact	The factor to scale the stand. dev. of the MSEP values when selecting the optimal number of components. For the "one standard error rule", sdfact is 1.

Details

The fitness of a variable subset is assessed by how well a PLS model fits the data. To estimate the optimal number of components for the PLS model, cross-validation is used.

Value

Returns an S4 object of type GenAlgFitEvaluator to be used as argument to a call of genAlg.

See Also

Other GenAlg Evaluators: evaluatorLM(), evaluatorPLS(), evaluatorUserFunction()

Examples

ctrl <- genAlgControl(populationSize = 200, numGenerations = 30, minVariables = 5,
    maxVariables = 12, verbosity = 1)
evaluator <- evaluatorFit(statistic = "BIC", numThreads = 1)

# Generate demo-data
set.seed(12345)
X <- matrix(rnorm(10000, sd = 1:5), ncol = 50, byrow = TRUE)
y <- drop(-1.2 + rowSums(X[, seq(1, 43, length = 8)]) + rnorm(nrow(X), 1.5));

result <- genAlg(y, X, control = ctrl, evaluator = evaluator, seed = 123)

subsets(result, 1:5)

gaselect documentation built on June 22, 2024, 11:05 a.m.