Summary method for forecast

summary method for class "fv_forecast".

Usage

# S3 method for fv_forecast
summary(
  object,
  target = "posterior",
  type = "model",
  as_dt = FALSE,
  forecast = FALSE,
  ...
)

Arguments

object

A data.table output from forecast() of class "fv_forecast".

target

A character string indicating the target object within the forecast() to summarise. Current options are: posterior predictions ("posterior"), posterior forecasts ("forecast"), the model fit ("fit"), and the model diagnostics ("diagnostics"). When "posterior" or "forecast" are used then summary.fv_posterior() is called on the nested posterior or forecast.

type

A character string used to filter the summarised output and defaulting to "model". Current options are: "model" which returns a summary of key model parameters, "cases" which returns summarised cases, "voc_frac" which returns summarised estimates of the fraction of cases that have the variant of concern, "voc_advantage" that returns summarised estimates of the the transmission advantage of the variant of concern, "growth" which returns summarised variant specific and overall growth rates, "rt" which returns summarised variant specific and overall reproduction number estimates, "raw" which returns a raw posterior summary, and "all" which returns all tidied posterior estimates.

as_dt

Logical defaults to FALSE. Once any filtering has been applied should summary() fall back to using the default data.table method.

forecast

Logical defaults to FALSE. Should fv_extract_forecast() be used to return only forecasts rather than complete posterior.

...

Additional summary arguments.

Value

A summary data.table.

See also

summary.fv_posterior forecast unnest_posterior

Functions used for forecasting across models, dates, and scenarios forecast_across_dates(), forecast_across_scenarios(), forecast_n_strain(), forecast(), plot.fv_forecast(), unnest_posterior()

Examples

if (FALSE) { # interactive()
options(mc.cores = 4)

forecasts <- forecast(
  germany_covid19_delta_obs,
  forecast_date = as.Date("2021-06-12"),
  horizon = 4,
  strains = c(1, 2),
  adapt_delta = 0.99,
  max_treedepth = 15,
  variant_relationship = "scaled"
)
# inspect forecasts
forecasts

# extract the model summary
summary(forecasts, type = "model")

# extract the fit object
summary(forecasts, target = "fit")

# extract the case forecast
summary(forecasts, type = "cases", forecast = TRUE)
}