R/get_covidhub_predictions.R
get_forecaster_predictions.RdSimply converts the predictions of forecasters submitting to the COVID Hub to the format of a predictions card, so it can be easily evaluated and compared.
get_forecaster_predictions( covidhub_forecaster_name, forecast_dates = NULL, geo_values = "*", forecast_type = c("point", "quantile"), ahead = 1:4, incidence_period = c("epiweek", "day"), signal = c("confirmed_incidence_num", "deaths_incidence_num", "deaths_cumulative_num", "confirmed_admissions_covid_1d") )
| covidhub_forecaster_name | String indicating of the forecaster (matching what it is called on the COVID Hub). |
|---|---|
| forecast_dates | Vector of Date objects (or strings of the form
"YYYY-MM-DD") indicating dates on which forecasts will be made. If |
| geo_values | vector of character strings containing FIPS codes of counties, or lower case state abbreviations (or "us" for national). The default "*" fetches all available locations |
| forecast_type | "quantile", "point" or both (the default) |
| ahead | number of periods ahead for which the forecast is required. NULL will fetch all available aheads |
| incidence_period | one of "epiweek" or "day". NULL will attempt to return both |
| signal | this function supports only "confirmed_incidence_num", "deaths_incidence_num", "deaths_cumulative_num", and/or "confirmed_admissions_covid_1d". For other types, use one of the alternatives mentioned above |
Long data frame of forecasts with a class of predictions_cards.
The first 4 columns are the same as those returned by the forecaster. The
remainder specify the prediction task, 10 columns in total:
ahead, geo_value, quantile, value, forecaster, forecast_date,
data_source, signal, target_end_date, and incidence_period. Here
data_source and signal correspond to the response variable only.
Predictions card. For more flexible processing of COVID Hub data, try using zoltr