Description Usage Arguments Value

View source: R/stacking-utils.R

Fit a stacking model that assigns weights to component models The weights are a function of observed covariates (which?), and are obtained via gradient tree boosting

1 2 3 4 5 6 7 8 9 | ```
fit_stacked_model(prediction_target, component_model_names,
explanatory_variables = c("analysis_time_season_week",
"kcde_model_confidence", "sarima_model_confidence", "weighted_ili"),
loso_preds_path, seasons_to_leave_out, booster = "gbtree", subsample = 1,
colsample_bytree = 1, colsample_bylevel = 1, max_depth = 10,
min_child_weight = -10^10, eta = 0.3, gamma = 0, lambda = 0,
alpha = 0, nrounds = 10, cv_params = NULL, cv_folds = NULL,
cv_nfolds = 10L, cv_refit = "ttest", update = NULL, nthread = NULL,
verbose = 0)
``` |

`prediction_target` |
string with either "onset", "peak_week", "peak_inc", "ph1_inc", ..., "ph4_inc" |

`component_model_names` |
character vector with names of component models |

`explanatory_variables` |
character vector with names of explanatory variables to include for weights; a non-empty subset of "analysis_time_season_week", "kcde_model_confidence", "sarima_model_confidence", "weighted_ili" |

`loso_preds_path` |
path to directory with leave-one-season-out predictions from each component model. Predictions should be saved in files named like "kde-National-loso-predictions.rds" |

`seasons_to_leave_out` |
optional character vector of seasons to leave out of stacking estimation |

`booster` |
what form of boosting to use? see xgboost documentation |

`subsample` |
fraction of data to use in bagging. not supported yet. |

`colsample_bytree` |
fraction of explanatory variables to randomly select in growing each regression tree. see xgboost documentation |

`colsample_bylevel` |
fraction of explanatory variables to randomly select in growing each level of the regression tree. see xgboost documentation |

`max_depth` |
maximum depth of regression trees. see xgboost documentation |

`min_child_weight` |
not recommended for use. see xgboost documentation |

`eta` |
learning rate. see xgboost documentation |

`gamma` |
Penalty on number of regression tree leafs. see xgboost documentation |

`lambda` |
L2 regularization of contribution to model weights in each round. see xgboost documentation |

`alpha` |
L1 regularization of contribution to model weights in each round. see xgboost documentation |

`nrounds` |
see xgboost documentation |

`cv_params` |
optional named list of parameter values to evaluate loss via cross-validation. Each component is a vector of parameter values with name one of "booster", "subsample", "colsample_bytree", "colsample_bylevel", "max_depth", "min_child_weight", "eta", "gamma", "lambda", "alpha", "nrounds" |

`cv_folds` |
list specifying observation groups to use in cross-validation each list component is a numeric vector of observation indices. |

`cv_nfolds` |
integer specifying the number of cross-validation folds to use. if cv_folds was provided, cv_nfolds is ignored. if cv_folds was not provided, the data will be randomly partitioned into cv_nfolds groups |

`cv_refit` |
character describing which of the models specified by the values in cv_params to refit using the full data set. Either "best", "ttest", or "none". |

`update` |
an object of class xgbstack to update |

`nthread` |
how many threads to use. see xgboost documentation |

`verbose` |
how much output to generate along the way. 0 for no logging, 1 for some logging |

a model stacking fit

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