`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.

## 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)
}
```