Skip to contents

ARX forecaster argument constructor

Source: R/arx_forecaster.R
arx_args_list.Rd

Constructs a list of arguments for arx_forecaster().

Usage

arx_args_list(
  lags = c(0L, 7L, 14L),
  ahead = 7L,
  n_training = Inf,
  forecast_date = NULL,
  target_date = NULL,
  adjust_latency = c("none", "extend_ahead", "extend_lags", "locf"),
  warn_latency = TRUE,
  quantile_levels = c(0.05, 0.95),
  symmetrize = TRUE,
  nonneg = TRUE,
  quantile_by_key = character(0L),
  check_enough_data_n = NULL,
  check_enough_data_epi_keys = NULL,
  ...
)

Arguments

lags

Vector or List. Positive integers enumerating lags to use in autoregressive-type models (in days). By default, an unnamed list of lags will be set to correspond to the order of the predictors.

ahead

Integer. Number of time steps ahead (in days) of the forecast date for which forecasts should be produced.

n_training

Integer. An upper limit for the number of rows per key that are used for training (in the time unit of the epi_df).

forecast_date

Date. The date from which the forecast is occurring. The default NULL will determine this automatically from either

  1. the maximum time value for which there's data if there is no latency adjustment (the default case), or

  2. the as_of date of epi_data if adjust_latency is non-NULL.

target_date

Date. The date that is being forecast. The default NULL will determine this automatically as forecast_date + ahead.

adjust_latency

Character. One of the methods of step_adjust_latency(), or "none" (in which case there is no adjustment). If the forecast_date is after the last day of data, this determines how to shift the model to account for this difference. The options are:

  • "none" the default, assumes the forecast_date is the last day of data

  • "extend_ahead": increase the ahead by the latency so it's relative to the last day of data. For example, if the last day of data was 3 days ago, the ahead becomes ahead+3.

  • "extend_lags": increase the lags so they're relative to the actual forecast date. For example, if the lags are c(0,7,14) and the last day of data was 3 days ago, the lags become c(3,10,17).

warn_latency

by default, step_adjust_latency warns the user if the latency is large. If this is FALSE, that warning is turned off.

quantile_levels

Vector or NULL. A vector of probabilities to produce prediction intervals. These are created by computing the quantiles of training residuals. A NULL value will result in point forecasts only.

symmetrize

Logical. The default TRUE calculates symmetric prediction intervals. This argument only applies when residual quantiles are used. It is not applicable with trainer = quantile_reg(), for example.

nonneg

Logical. The default TRUE enforces nonnegative predictions by hard-thresholding at 0.

quantile_by_key

Character vector. Groups residuals by listed keys before calculating residual quantiles. See the by_key argument to layer_residual_quantiles() for more information. The default, character(0) performs no grouping. This argument only applies when residual quantiles are used. It is not applicable with trainer = quantile_reg(), for example.

check_enough_data_n

Integer. A lower limit for the number of rows per epi_key that are required for training. If NULL, this check is ignored.

check_enough_data_epi_keys

Character vector. A character vector of column names on which to group the data and check threshold within each group. Useful if training per group (for example, per geo_value).

...

Space to handle future expansions (unused).

Value

A list containing updated parameter choices with class arx_flist.

Examples

arx_args_list()
#> • lags : 0, 7, and 14
#> • ahead : 7
#> • n_training : Inf
#> • quantile_levels : 0.05 and 0.95
#> • forecast_date : "NULL"
#> • target_date : "NULL"
#> • adjust_latency : "none"
#> • warn_latency : TRUE
#> • symmetrize : TRUE
#> • nonneg : TRUE
#> • max_lags : 14
#> • quantile_by_key : "_empty_"
#> • check_enough_data_n : "NULL"
#> • check_enough_data_epi_keys : "NULL"
arx_args_list(symmetrize = FALSE)
#> • lags : 0, 7, and 14
#> • ahead : 7
#> • n_training : Inf
#> • quantile_levels : 0.05 and 0.95
#> • forecast_date : "NULL"
#> • target_date : "NULL"
#> • adjust_latency : "none"
#> • warn_latency : TRUE
#> • symmetrize : FALSE
#> • nonneg : TRUE
#> • max_lags : 14
#> • quantile_by_key : "_empty_"
#> • check_enough_data_n : "NULL"
#> • check_enough_data_epi_keys : "NULL"
arx_args_list(quantile_levels = c(.1, .3, .7, .9), n_training = 120)
#> • lags : 0, 7, and 14
#> • ahead : 7
#> • n_training : 120
#> • quantile_levels : 0.1, 0.3, 0.7, and 0.9
#> • forecast_date : "NULL"
#> • target_date : "NULL"
#> • adjust_latency : "none"
#> • warn_latency : TRUE
#> • symmetrize : TRUE
#> • nonneg : TRUE
#> • max_lags : 14
#> • quantile_by_key : "_empty_"
#> • check_enough_data_n : "NULL"
#> • check_enough_data_epi_keys : "NULL"