MQR_qreg_mboost: Multiple Quantile Regression using 'mboost' (depreciated)
In jbrowell/ProbCast: Flexible Probabilistic Forecasting

MQR_qreg_mboost

R Documentation

Multiple Quantile Regression using `mboost` (depreciated)

Description

This function is now depreciated and may be removed in future versions of this package. Use mqr_qreg_mboost() instead.

Usage

MQR_qreg_mboost(
  data,
  formula,
  quantiles = c(0.25, 0.5, 0.75),
  CVfolds = NULL,
  ...,
  bc_mstop = 100,
  bc_nu = 0.1,
  w = rep(1, nrow(data)),
  parallel = F,
  cores = NULL,
  pckgs = NULL,
  Sort = T,
  SortLimits = NULL,
  save_models_path = NULL
)

Arguments

`data`	A `data.frame` containing target and explanatory variables. May optionally contain a column called "kfold" with numbered/labeled folds and "Test" for test data.
`quantiles`	A vector with length>=2 containing the quantiles to fit models for.
`CVfolds`	Control for cross-validation if not supplied in `data`.
`parallel`	`boolean` parallelize cross-validation process? Parallelisation is over cross-validation folds.
`cores`	if parallel is TRUE then number of available cores
`pckgs`	if parallel is TRUE then specify packages required for each worker (e.g. c("data.table) if data stored as such)
`Sort`	`boolean` Sort quantiles using `SortQuantiles()`?
`SortLimits`	`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.
`formaula`	A `formula` object with the response on the left of an ~ operator, and the terms, separated by + operators, on the right.

Details

Jethro Browell, jethro.browell@strath.ac.uk

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.