evaluatorFit {gaselect} | R Documentation |
Creates the object that controls the evaluation step in the genetic algorithm
evaluatorFit(numSegments = 7L, statistic = c("BIC", "AIC", "adjusted.r.squared", "r.squared"), numThreads = NULL, maxNComp = NULL, sdfact = 1)
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", |
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.
Returns an S4 object of type GenAlgFitEvaluator
to be used as argument to
a call of genAlg
.
Other GenAlg Evaluators: evaluatorLM
,
evaluatorPLS
,
evaluatorUserFunction
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)