View source: R/Roy_Larocque_2019.R

Currently implemented is the quantile method with BOP intervals. Used inside rfint().

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | ```
RoyRF(
formula = NULL,
train_data = NULL,
pred_data = NULL,
num_trees = NULL,
min_node_size = NULL,
m_try = NULL,
keep_inbag = TRUE,
intervals = TRUE,
interval_method = "quantile",
calibrate = FALSE,
alpha = NULL,
num_threads = NULL,
tolerance = NULL,
step_percent = NULL,
under = NULL,
method = NULL,
max_iter = NULL,
interval_type = NULL
)
``` |

`formula` |
Object of class formula or character describing the model to fit. Interaction terms supported only for numerical variables. |

`train_data` |
Training data of class data.frame, matrix, dgCMatrix (Matrix) or gwaa.data (GenABEL). Matches ranger() requirements. |

`pred_data` |
Test data of class data.frame, matrix, dgCMatrix (Matrix) or gwaa.data (GenABEL). Utilizes ranger::predict() to get prediction intervals for test data. |

`num_trees` |
Number of trees. |

`min_node_size` |
Minimum number of observations before split at a node. |

`m_try` |
Number of variables to randomly select from at each split. |

`keep_inbag` |
Saves matrix of observations and which tree(s) they occur in. Required to be true to generate variance estimates for Ghosal, Hooker 2018 method. *Should not be an option... |

`intervals` |
Generate prediction intervals or not. |

`interval_method` |
which prediction interval type to generate. Several outlined in paper; currently only one method implemented. |

`calibrate` |
calibrate prediction intervals based on out-of-bag performance. Adjusts alpha to get nominal coverage. |

`alpha` |
Significance level for prediction intervals. |

`num_threads` |
The number of threads to use in parallel. Default is the current number of cores. |

`interval_type` |
Type of prediction interval to generate.
Options are |

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.