| evaluatorFit {gaselect} | R Documentation |
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", |
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)