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Climatological forecaster argument constructor

Source: R/climatological_forecaster.R
climate_args_list.Rd

Climatological forecaster argument constructor

Usage

climate_args_list(
  forecast_date = NULL,
  forecast_horizon = 0:4,
  time_type = c("epiweek", "week", "month", "day"),
  center_method = c("median", "mean"),
  window_size = 3L,
  quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 0.95),
  symmetrize = FALSE,
  nonneg = TRUE,
  quantile_by_key = character(0L),
  ...
)

Arguments

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.

forecast_horizon

Vector of integers giving the number of time steps, in units of the time_type, from the reference_date for which predictions should be produced.

time_type

The duration over which time aggregation should be performed.

center_method

The measure of center to be calculated over the time window.

window_size

Scalar integer. How many time units on each side should be included. For example, if window_size = 3 and time_type = "day", then on each day in the data, the center will be calculated using 3 days before and three days after. So, in this case, it operates like a weekly rolling average, centered at each day.

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. Typically, one would only want non-symmetric quantiles when increasing trajectories are quite different from decreasing ones, such as a strictly postive variable near zero.

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.

...

Further arguments passed to or from other methods (not currently used).

Value

A list containing updated parameter choices with class climate_alist.

See also

climatological_forecaster(), step_climate()

Examples

climate_args_list()
#> • forecast_date : "NULL"
#> • forecast_horizon : 0, 1, 2, 3, and 4
#> • time_type : "epiweek"
#> • center_method : "median"
#> • window_size : 3
#> • quantile_levels : 0.05, 0.1, 0.25, 0.5, 0.75, 0.9, and 0.95
#> • symmetrize : FALSE
#> • nonneg : TRUE
#> • quantile_by_key : "_empty_"
climate_args_list(
  forecast_horizon = 0:10,
  quantile_levels = c(.01, .025, 1:19 / 20, .975, .99)
)
#> • forecast_date : "NULL"
#> • forecast_horizon : 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, and 10
#> • time_type : "epiweek"
#> • center_method : "median"
#> • window_size : 3
#> • quantile_levels : 0.01, 0.025, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4,
#>   0.45, 0.5, 0.55, 0.6, 0.65, 0.7, 0.75, 0.8, …, 0.975, and 0.99
#> • symmetrize : FALSE
#> • nonneg : TRUE
#> • quantile_by_key : "_empty_"