qreg_mboost: Multiple Quantile Regression using 'mboost'
In jbrowell/ProbCast: Flexible Probabilistic Forecasting

qreg_mboost

R Documentation

Multiple Quantile Regression using `mboost`

Description

This function fits multiple quantile regression models using mboost, with facilities for cross-validation. mboost accommodates both generalised additive models, decision trees and other learners. See ?mboost for more details.

Usage

qreg_mboost(
  data,
  formula,
  quantiles = c(0.25, 0.5, 0.75),
  cv_folds = NULL,
  w = rep(1, nrow(data)),
  cores = 1,
  pckgs = NULL,
  sort = T,
  sort_limits = NULL,
  save_models_path = NULL,
  only_mqr = FALSE,
  exclude_train = NULL,
  ...
)

Arguments

`data`	A `data.frame` containing target and explanatory variables.
`quantiles`	The quantiles to fit models for.
`cv_folds`	Control for cross-validation with various options, either: the column name of the fold index supplied in data. Observations and inputs in the index labelled "Test" will serve as test data and held out in model training. an integer giving the number of cross validation folds to generate. Folds are constructed as block chunks. Default behaviour is 5 folds. vector of length==nrow(data) containing character or numeric fold labels. NULL indicates that no cross validation should be performed and the returned model is trained on all `data`.
`w`	an optional numeric vector of weights to be used in the fitting process.
`cores`	the number of available cores. Defaults to one, i.e. no parallelisation, although in this case the user must still specify `pckgs` if applicable.
`pckgs`	specify additional packages required for each worker (e.g. c("data.table") if data stored as such).
`sort`	Sort quantiles using `SortQuantiles()`?
`sort_limits`	`Limits` argument to be passed to `SortQuantiles()`. Constrains quantiles to upper and lower limits given by `list(U=upperlim,L=lowerlim)`.
`save_models_path`	Path to save models. Model details and file extension pasted onto this string. Defaults to `NULL`, i.e. no model save.
`only_mqr`	return only the out-of-sample predictions?
`exclude_train`	control for exclusion of rows in data for the model training only, with various options, either: the column name of the binary/boolean exclude flag supplied in data. a vector of binary/boolean exclusion flags of length nrow(data) NULL indicates no exclusion This option is useful when out-of-sample predictions are required in rows which need excluded during model training
`...`	extra hyper-parameters to be passed to `mboost()`. e.g. use `control = mboost::boost_control()` to specify boosting steps and shrinkage.
`formaula`	A `formula` object with the response on the left of an ~ operator, and the terms, separated by + operators, on the right.

Details

The returned predictive quantiles are those produced out-of-sample for each cross-validation fold (using models trained on the remaining folds but not "Test" data). Predictive quantiles corresponding to "Test" data are produced using models trained on all non-test data.

The returned models are in a named list corresponding to the model for each fold and and can be extracted for further prediction or evaluation. See predict.qreg_mboost().

Value

by default a named list containing fitted models as a list of qreg_mboost objects, and out-of-sample cross validation forecasts as an MultiQR object. The output list depends on cv_folds.

Alternatively returns only the out-of-sample cross validation forecasts as an MultiQR object when only_mqr is TRUE