Package 'qrensemble'

Quantile Regression Average Calculates a quantile regression average for forecasts.

Description

Quantile Regression Average Calculates a quantile regression average for forecasts.

Usage

qra(
  forecast,
  target,
  group = c(),
  model = "Quantile Regression Average",
  per_quantile_weights = FALSE,
  enforce_normalisation = TRUE,
  intercept = FALSE,
  noncross = TRUE,
  ...
)

Arguments

forecast	a data.table representing forecast; this is expected to have been created using scoringutils::as_forecast_quantile()
target	the target for which to create the quantile regression average. This should be given as a vector of form column = target, where target is the value of column that represents the target. Note that the column named here cannot be a grouping variable.
group	any columns wihch to group a vector of character vectors (e.g., "horizon", "geography_scale", etc.) indicating columns in the forecasts and data data frames; by default, will not group anything, i.e. create one ensemble model
model	the name of the model to return; default: "Quantile Regression Average"
per_quantile_weights	logical; whether to estimate weights per quantile
enforce_normalisation	logical; whether to enforce quantiles
intercept	logical; whether to estimate and intercept
noncross	logical; whether ot enforce non-crosssing of quantiles
...	passed to quantgen::predict.quantile_ensemble(); of particular interest might be setting iso = TRUE for isotonic regression

Value

a data.table representing the forecasts forecast, but with model set to the value of the 'model parameter. This will be in the forecast format produced by scoringutils::as_forecast_quantile()

Examples

library("scoringutils")
example_quantile |>
  as_forecast_quantile() |>
  qra(
    group = c("target_type", "location", "location_name"),
    target = c(target_end_date = "2021-07-24")
  )

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