qreg_mrq: Multiple quantile regression, and quantile dressing, using...
In jbrowell/ProbCast: Flexible Probabilistic Forecasting

qreg_mrq

R Documentation

Multiple quantile regression, and quantile dressing, using linear quantile regression

Description

This function fits multiple conditional linear quantile regression models, optionally to the residuals of a user-specified deterministic forecast with facilities for cross-validation.

Usage

qreg_mrq(
  data,
  formula,
  quantiles = c(0.25, 0.5, 0.75),
  offset = NULL,
  cv_folds = NULL,
  exclude_train = NULL,
  sort = T,
  sort_limits = NULL,
  ...
)

Arguments

`data`	A `data.frame` containing target and explanatory variables. Optionally, supplying a deterministic forecast with `offset` will return the this forecast dressed with multiple predictive quantiles. May optionally contain a column called "kfold" with numbered/labelled folds and "Test" for test data.
`quantiles`	The quantiles to fit models for.
`offset`	The column name in `data` of an optional deterministic forecast to be dressed with multiple predictive quantiles.
`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. NULL indicates that no cross validation should be performed and the returned model is trained on all `data`.
`exclude_train`	A column name in `data` indicating if a row should be excluded from model training, i.e. if it contains bad data (will be coerced to `logical`). Alternatively, an `integer` or `logical` vector with length equal to the number of rows in `data` indicating the same. Rows labelled `TRUE` are excluded from model training.
`sort`	`boolean` 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)`.
`...`	Additional arguments passed to `rq`.
`formala`	A `formula` object with the response on the left of an ~ operator, and the terms, separated by + operators, on the right passed to `rq()`.

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.