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

1 2 3 | ```
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`

1 2 3 4 5 6 7 8 9 10 11 12 | ```
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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