Description Usage Arguments Details Value See Also Examples

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

1 2 3 | ```
evaluatorPLS(numReplications = 30L, innerSegments = 7L,
outerSegments = 1L, testSetSize = NULL, numThreads = NULL,
maxNComp = NULL, method = c("simpls"), sdfact = 1)
``` |

`numReplications` |
The number of replications used to evaluate a variable subset (must be between 1 and 2^16) |

`innerSegments` |
The number of CV segments used in one replication (must be between 2 and 2^16) |

`outerSegments` |
The number of outer CV segments used in one replication (between 0 and 2^16). If this is greater than 1, repeated double cross-validation strategy (rdCV) will be used instead of simple repeated cross-validation (srCV) (see details) |

`testSetSize` |
The relative size of the test set used for simple repeated CV (between 0 and 1). This parameter is ignored if outerSegments > 1 and a warning will be issued. |

`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) |

`method` |
The PLS method used to fit the PLS model (currently only SIMPLS is implemented) |

`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", |

With this method the genetic algorithm uses PLS regression models to assess the prediction power of
variable subsets. By default, simple repeated cross-validation (srCV) is used. The optimal number
of PLS components is estimated using cross-validation (with `innerSegments`

segments) on a
training set. The prediction power is then evaluated by fitting a PLS regression model with this optimal
number of components to the training set and predicting the values of a test set (of either
`testSetSize`

size or `1 / innerSegments`

, if `testSetSize`

is not specified).

If the parameter `outerSegments`

is given, repeated double cross-validation is used instead.
There, the data set is first split into `outerSegments`

segments and one segment is used as
prediction set and the other segments as test set. This is repeated for each outer segment.

The whole procedure is repeated `numReplications`

times to get a more reliable estimate of the
prediction power.

Returns an S4 object of type `GenAlgPLSEvaluator`

to be used as argument to
a call of `genAlg`

.

Other GenAlg.Evaluators: `evaluatorFit`

;
`evaluatorLM`

;
`evaluatorUserFunction`

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 | ```
ctrl <- genAlgControl(populationSize = 100, numGenerations = 15, minVariables = 5,
maxVariables = 12, verbosity = 1)
evaluatorSRCV <- evaluatorPLS(numReplications = 2, innerSegments = 7, testSetSize = 0.4,
numThreads = 1)
evaluatorRDCV <- evaluatorPLS(numReplications = 2, innerSegments = 5, outerSegments = 3,
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));
resultSRCV <- genAlg(y, X, control = ctrl, evaluator = evaluatorSRCV, seed = 123)
resultRDCV <- genAlg(y, X, control = ctrl, evaluator = evaluatorRDCV, seed = 123)
subsets(resultSRCV, 1:5)
subsets(resultRDCV, 1:5)
``` |

```
Generating initial population
Generating generation 1
Generating generation 2
Generating generation 3
Generating generation 4
Generating generation 5
Generating generation 6
Generating generation 7
Generating generation 8
Generating generation 9
Generating generation 10
Generating generation 11
Generating generation 12
Generating generation 13
Generating generation 14
Generating generation 15
Generating initial population
Generating generation 1
Generating generation 2
Generating generation 3
Generating generation 4
Generating generation 5
Generating generation 6
Generating generation 7
Generating generation 8
Generating generation 9
Generating generation 10
Generating generation 11
Generating generation 12
Generating generation 13
Generating generation 14
Generating generation 15
$`1`
[1] 1 5 6 7 13 16 19 20 25 37 43 47
$`2`
[1] 1 5 6 7 13 16 19 25 36 37 43
$`3`
[1] 1 5 6 7 13 16 19 25 36 37 43 46
$`4`
[1] 1 5 7 13 19 25 36 37 43 47
$`5`
[1] 1 5 6 7 13 16 19 20 25 36 37 43
$`1`
[1] 1 7 11 13 19 25 33 36 37 42 43 49
$`2`
[1] 1 7 11 13 15 19 25 33 36 37 42 43
$`3`
[1] 1 7 11 13 14 19 25 33 36 37 42 43
$`4`
[1] 1 7 11 13 19 25 37 42 43
$`5`
[1] 1 6 7 13 19 25 37 42 43
```

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