Description Usage Arguments Details Value Author(s) References See Also Examples

The main function for the mogavs genetic algorithm, returning a list containing the full archive set of regression models tried and the nondominated set.

1 2 3 4 5 6 7 8 | ```
## Default S3 method:
mogavs(x, y, maxGenerations = 10*ncol(x), popSize = ncol(x), noOfOffspring = ncol(x),
crossoverProbability = 0.9, mutationProbability = 1/ncol(x), kBest = 1,
plots = F, additionalPlots = F, ...)
## S3 method for class 'formula'
mogavs(formula, data, maxGenerations= 10*ncol(x), popSize = ncol(x),
noOfOffspring = ncol(x), crossoverProbability = 0.9, mutationProbability = 1/ncol(x),
kBest = 1, plots = F, additionalPlots = F, ...)
``` |

`formula` |
Formula interface with y~x1+x2 or y~. for predicting y with x1 and x2 or all predictors, respectively. |

`data` |
A data frame containing the variables mentioned in the formula. |

`x` |
An n x p matrix containing the n observations of p values used in the regression. |

`y` |
An n x 1 vector of values to fit the regression to. |

`maxGenerations` |
Number of maximum generations to be run in the evolutionary algorithm. Default is 10*ncol(x) |

`popSize` |
Population size, ie. how many regression models the population holds. Default is ncol(x). |

`noOfOffspring` |
Indicates how many offspring models are generated for each generation. Default is ncol(x). |

`crossoverProbability` |
Indicates the probability of crossover for each offpring. Default is 0.9. |

`mutationProbability` |
Indicates the probability of mutation for each offspring. Default is 1/ncol(x). |

`kBest` |
Indicates how many best models for each number of variables are highlighted in printing at the end of the run (default=1). |

`plots` |
Binary variable for turning plotting for each generation on/off. |

`additionalPlots` |
Binary variable for turning additional plotting at the end of the run on/off. Plot can also be generated after the run with given |

`...` |
Any additional arguments. |

Runs genetic algorithm for the linear regression model space, with predicting variables x and predicted values y. Alternatively, can be given a data frame and formula. Setting `plots=TRUE`

creates for each generation a plot, showing the current efficient boundary of the models. Setting `additionalPlots=TRUE`

gives out an additional plot at the end of the algorithm, showing the full set of tried models and the `kBest`

best models for each number of variables. All plotting is turned off by default to make processing faster.

Returns model of class `mogavs`

with items

`nonDominatedSet` |
Matrix of the nondominated models. |

`numOfVariables` |
Vector of the number of variables for each model in the nonDominatedSet. |

`MSE` |
Vector of mean square errors for each model in the nonDominatedSet. |

`archiveSet` |
The full archive set of models tried |

`kBest` |
The value of kBest used |

`maxGenerations` |
Number of generations used. |

`crossoverProbability` |
The crossover probability used. |

`noOfOffspring` |
Number of generated offspring for each generation. |

`popSize` |
The population size. |

Tommi Pajala <tommi.pajala@aalto.fi>

Sinha, A., Malo, P. & Kuosmanen, T. (2015) A Multi-objective Exploratory Procedure for Regression Model Selection. *Journal of Computational and Grahical Statistics, 24(1). pp. 154-182.*

1 2 3 4 5 6 | ```
data(sampleData)
#just a few generations to keep test fast
mogavs(y~.,data=sampleData,maxGenerations=5)
#with a more sensible number of generations, with all plotting on
## Not run: mogavs(y~.,data=sampleData,maxGenerations=100,plots=TRUE,additionalPlots=TRUE)
``` |

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