evaluatorLM | R Documentation |
Create an evaluator that uses a linear model to evaluate the fitness.
evaluatorLM( statistic = c("BIC", "AIC", "adjusted.r.squared", "r.squared"), numThreads = NULL )
statistic |
The statistic used to evaluate the fitness |
numThreads |
The maximum number of threads the algorithm is allowed to spawn (a value less than 1 or NULL means no threads) |
Different statistics to evaluate the fitness of the variable subset can be given. If a maximum absolute correlation is given the algorithm will be very slow (as the C++ implementation can not be used anymore) and multithreading is not available.
Returns an S4 object of type GenAlgLMEvaluator
Other GenAlg Evaluators:
evaluatorFit()
,
evaluatorPLS()
,
evaluatorUserFunction()
ctrl <- genAlgControl(populationSize = 200, numGenerations = 30, minVariables = 5, maxVariables = 12, verbosity = 1) evaluator <- evaluatorLM(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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