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ARX classifier argument constructor

Source: R/arx_classifier.R
arx_class_args_list.Rd

Constructs a list of arguments for arx_classifier().

Usage

arx_class_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,
  outcome_transform = c("growth_rate", "lag_difference"),
  breaks = 0.25,
  horizon = 7L,
  method = c("rel_change", "linear_reg"),
  log_scale = FALSE,
  additional_gr_args = list(),
  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.

outcome_transform

Scalar character. Whether the outcome should be created using growth rates (as the predictors are) or lagged differences. The second case is closer to the requirements for the 2022-23 CDC Flusight Hospitalization Experimental Target. See the Classification Vignette for details of how to create a reasonable baseline for this case. Selecting "growth_rate" (the default) uses epiprocess::growth_rate() to create the outcome using some of the additional arguments below. Choosing "lag_difference" instead simply uses the change from the value at the selected horizon.

breaks

Vector. A vector of breaks to turn real-valued growth rates into discrete classes. The default gives binary upswing classification as in McDonald, Bien, Green, Hu, et al.. This coincides with the default trainer = parsnip::logistic_reg() argument in arx_classifier(). However, multiclass classification is also supported (e.g. with breaks = c(-.2, .25)) provided that trainer = parsnip::multinom_reg() (or another multiclass trainer) is used as well. These will be sliently expanded to cover the entire real line (so the default will become breaks = c(-Inf, .25, Inf)) before being used to discretize the response. This is different than the behaviour in recipes::step_cut() which creates classes that only cover the range of the training data.

horizon

Scalar integer. This is passed to the h argument of epiprocess::growth_rate(). It determines the amount of data used to calculate the growth rate.

method

Character. Options available for growth rate calculation.

log_scale

Scalar logical. Whether to compute growth rates on the log scale.

additional_gr_args

List. Optional arguments controlling growth rate calculation. See epiprocess::growth_rate() and the related Vignette for more details.

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_clist.

Examples

arx_class_args_list()
#> • lags : 0, 7, and 14
#> • ahead : 7
#> • n_training : Inf
#> • breaks : -Inf, 0.25, and Inf
#> • forecast_date : "NULL"
#> • target_date : "NULL"
#> • adjust_latency : "none"
#> • outcome_transform : "growth_rate"
#> • max_lags : 14
#> • horizon : 7
#> • method : "rel_change"
#> • log_scale : FALSE
#> • additional_gr_args : "_empty_"
#> • check_enough_data_n : "NULL"
#> • check_enough_data_epi_keys : "NULL"

# 3-class classsification,
# also needs arx_classifier(trainer = parsnip::multinom_reg())
arx_class_args_list(breaks = c(-.2, .25))
#> • lags : 0, 7, and 14
#> • ahead : 7
#> • n_training : Inf
#> • breaks : -Inf, -0.2, 0.25, and Inf
#> • forecast_date : "NULL"
#> • target_date : "NULL"
#> • adjust_latency : "none"
#> • outcome_transform : "growth_rate"
#> • max_lags : 14
#> • horizon : 7
#> • method : "rel_change"
#> • log_scale : FALSE
#> • additional_gr_args : "_empty_"
#> • check_enough_data_n : "NULL"
#> • check_enough_data_epi_keys : "NULL"