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CDC baseline forecaster argument constructor

Source: R/cdc_baseline_forecaster.R
cdc_baseline_args_list.Rd

Constructs a list of arguments for cdc_baseline_forecaster().

Usage

cdc_baseline_args_list(
  data_frequency = "1 week",
  aheads = 1:5,
  n_training = Inf,
  forecast_date = NULL,
  quantile_levels = c(0.01, 0.025, 1:19/20, 0.975, 0.99),
  nsims = 100000L,
  symmetrize = TRUE,
  nonneg = TRUE,
  quantile_by_key = "geo_value",
  nafill_buffer = Inf,
  ...
)

Arguments

data_frequency

Integer or string. This describes the frequency of the input epi_df. For typical FluSight forecasts, this would be "1 week". Allowable arguments are integers (taken to mean numbers of days) or a string like "7 days" or "2 weeks". Currently, all other periods (other than days or weeks) result in an error.

aheads

Integer vector. Unlike arx_forecaster(), this doesn't have any effect on the predicted values. Predictions are always the most recent observation. This determines the set of prediction horizons for layer_cdc_flatline_quantiles(). It interacts with the data_frequencyargument. So, for example, if the data is daily and you want forecasts for 1:4 days ahead, then you would use1:4. However, if you want one-week predictions, you would set this as c(7, 14, 21, 28). But if data_frequencyis"1 week", then you would set it as 1:4`.

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 on which the forecast is created. The default NULL will attempt to determine this automatically.

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.

nsims

Positive integer. The number of draws from the empirical CDF. These samples are spaced evenly on the (0, 1) scale, F_X(x) resulting in linear interpolation on the X scale. This is achieved with stats::quantile() Type 7 (the default for that function).

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. Force all predictive intervals be non-negative. Because non-negativity is forced before propagating forward, this has slightly different behaviour than would occur if using layer_threshold().

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.

nafill_buffer

At predict time, recent values of the training data are used to create a forecast. However, these can be NA due to, e.g., data latency issues. By default, any missing values will get filled with less recent data. Setting this value to NULL will result in 1 extra recent row (beyond those required for lag creation) to be used. Note that we require at least min(lags) rows of recent data per geo_value to create a prediction. For this reason, setting nafill_buffer < min(lags) will be treated as additional allowed recent data rather than the total amount of recent data to examine.

...

Space to handle future expansions (unused).

Value

A list containing updated parameter choices with class cdc_flat_fcast.

Examples

cdc_baseline_args_list()
#> • data_frequency : 7
#> • aheads : 1, 2, 3, 4, and 5
#> • n_training : Inf
#> • forecast_date : "NULL"
#> • 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
#> • nsims : 100000
#> • symmetrize : TRUE
#> • nonneg : TRUE
#> • quantile_by_key : "geo_value"
#> • nafill_buffer : Inf
cdc_baseline_args_list(symmetrize = FALSE)
#> • data_frequency : 7
#> • aheads : 1, 2, 3, 4, and 5
#> • n_training : Inf
#> • forecast_date : "NULL"
#> • 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
#> • nsims : 100000
#> • symmetrize : FALSE
#> • nonneg : TRUE
#> • quantile_by_key : "geo_value"
#> • nafill_buffer : Inf
cdc_baseline_args_list(quantile_levels = c(.1, .3, .7, .9), n_training = 120)
#> • data_frequency : 7
#> • aheads : 1, 2, 3, 4, and 5
#> • n_training : 120
#> • forecast_date : "NULL"
#> • quantile_levels : 0.1, 0.3, 0.7, and 0.9
#> • nsims : 100000
#> • symmetrize : TRUE
#> • nonneg : TRUE
#> • quantile_by_key : "geo_value"
#> • nafill_buffer : Inf