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GenAlgFitEvaluator-constructor: Fit Evaluator

Description Usage Arguments Details Value See Also Examples

Description

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

Usage

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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

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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)


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