var_selection_by_permute_cv {bartMachine} | R Documentation |

Performs variable selection by cross-validating over the three threshold-based procedures outlined in Bleich et al. (2013) and selecting the single procedure that returns the lowest cross-validation RMSE.

var_selection_by_permute_cv(bart_machine, k_folds = 5, num_reps_for_avg = 5, num_permute_samples = 100, num_trees_for_permute = 20, alpha = 0.05, num_trees_pred_cv = 50)

`bart_machine` |
An object of class “bartMachine”. |

`k_folds` |
Number of folds to be used in cross-validation. |

`num_reps_for_avg` |
Number of replicates to over over to for the BART model's variable inclusion proportions. |

`num_permute_samples` |
Number of permutations of the response to be made to generate the “null” permutation distribution. |

`num_trees_for_permute` |
Number of trees to use in the variable selection procedure. As with |

`alpha` |
Cut-off level for the thresholds. |

`num_trees_pred_cv` |
Number of trees to use for prediction on the hold-out portion of each fold. Once variables have been selected using the training portion of each fold, a new model is built using only those variables with |

See Bleich et al. (2013) for a complete description of the procedures outlined above as well as the corresponding vignette for a brief summary with examples.

Returns a list with the following components:

`best_method` |
The name of the best variable selection procedure, as chosen via cross-validation. |

`important_vars_cv` |
The variables chosen by the |

This function can have substantial run-time.
This function is parallelized by the number of cores set in `set_bart_machine_num_cores`

.

Adam Kapelner and Justin Bleich

J Bleich, A Kapelner, ST Jensen, and EI George. Variable Selection Inference for Bayesian Additive Regression Trees. ArXiv e-prints, 2013.

Adam Kapelner, Justin Bleich (2016). bartMachine: Machine Learning with Bayesian Additive Regression Trees. Journal of Statistical Software, 70(4), 1-40. doi:10.18637/jss.v070.i04

`var_selection_by_permute`

, `investigate_var_importance`

## Not run: #generate Friedman data set.seed(11) n = 150 p = 100 ##95 useless predictors X = data.frame(matrix(runif(n * p), ncol = p)) y = 10 * sin(pi* X[ ,1] * X[,2]) +20 * (X[,3] -.5)^2 + 10 * X[ ,4] + 5 * X[,5] + rnorm(n) ##build BART regression model (not actually used in variable selection) bart_machine = bartMachine(X, y) #variable selection via cross-validation var_sel_cv = var_selection_by_permute_cv(bart_machine, k_folds = 3) print(var_sel_cv$best_method) print(var_sel_cv$important_vars_cv) ## End(Not run)

[Package *bartMachine* version 1.2.6 Index]