GenAlgUserEvaluator-constructor: User Defined Evaluator

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

User Defined Evaluator

Description

Create an evaluator that uses a user defined function to evaluate the fitness

Usage

evaluatorUserFunction(FUN, sepFUN = NULL, ...)

Arguments

FUN	Function used to evaluate the fitness
sepFUN	Function to calculate the SEP of the variable subsets
...	Additional arguments passed to FUN and sepFUN

Details

The user specified function must take a the response vector as first and the covariates matrix as second argument. The function must return a number representing the fitness of the variable subset (the higher the value the fitter the subset) Additionally the user can specify a function that takes a GenAlg object and returns the standard error of prediction of the found variable subsets.

Value

Returns an S4 object of type GenAlgUserEvaluator

See Also

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

Examples

ctrl <- genAlgControl(populationSize = 100, numGenerations = 10, minVariables = 5,
    maxVariables = 12, verbosity = 1)

# Use the BIC of a linear model to evaluate the fitness of a variable subset
evalFUN <- function(y, X) {
		return(BIC(lm(y ~ X)));
}

# Dummy function that returns the residuals standard deviation and not the SEP
sepFUN <- function(genAlg) {
    return(apply(genAlg@subsets, 2, function(subset) {
		m <- lm(genAlg@response ~ genAlg@covariates[, subset]);
		return(sd(m$residuals));
	}));
}

evaluator <- evaluatorUserFunction(FUN = evalFUN, sepFUN = sepFUN)

# 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 Feb. 16, 2023, 6:14 p.m.