Constructor for the `dorem`

control object

dorem_control(
weights = NULL,
na.rm = TRUE,
loss_func = dorem_loss_func,
link_func = dorem_no_link_func,
perf_func = dorem_perf_func,
optim_method = valid_optimization_methods(),
optim_maxit = 1000,
optim_VTR = -Inf,
optim_grid_n = 5,
optim_trace = FALSE,
coefs_start = NULL,
coefs_lower = NULL,
coefs_upper = NULL,
cv_repeats = NULL,
cv_folds = NULL,
shuffle = FALSE,
iter = TRUE,
seed = NULL
)

## Arguments

weights |
Default is NULL |

na.rm |
Default is TRUE |

loss_func |
Function used in optimization objective function. Default is `dorem_loss_func`
which returns MSE |

link_func |
Function used to convert predictions. Default is `dorem_no_link_func`
which returns the original value |

perf_func |
Function used to quantify model fit. Default is `dorem_perf_func` |

optim_method |
Optimization method. The list of supported methods is `c("L-BFGS-B", "DE", "CMA-ES", "gridSearch")` |

optim_maxit |
Maximal number of iterations for the optimization method. Default is 1000 |

optim_VTR |
Value To Reach in the optimization method. Default is -Inf |

optim_grid_n |
Number of search values for each predictor used in gridSearch method. Default is 5 |

optim_trace |
Should optimization trace be shown? Default is FALSE |

coefs_start |
Starting values for coefficients |

coefs_lower |
Lower bound for coefficients |

coefs_upper |
Upper bound for coefficients |

cv_repeats |
Number of CV repeats |

cv_folds |
Number of CV folds |

shuffle |
Should shuffle be performed? Default is FALSE |

iter |
Should iter be shown? Default is TRUE |

seed |
Random number seed |

## Examples

#> Performing banister method using L-BFGS-B optimization

#> Training the model...

#> Cross-validating the model using 5 repeats of 3 folds

#> Fold1.Rep1...

#> Fold2.Rep1...

#> Fold3.Rep1...

#> Fold1.Rep2...

#> Fold2.Rep2...

#> Fold3.Rep2...

#> Fold1.Rep3...

#> Fold2.Rep3...

#> Fold3.Rep3...

#> Fold1.Rep4...

#> Fold2.Rep4...

#> Fold3.Rep4...

#> Fold1.Rep5...

#> Fold2.Rep5...

#> Fold3.Rep5...

#> Training the model using shuffled predictors...

#> Done!