Description Usage Arguments Value
View source: R/forecasting_helpers.R
Extend environmental data into the future.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 | extend_env_future(
env_data,
quo_groupfield,
quo_obsfield,
quo_valuefield,
env_ref_data,
env_info,
fc_model_family,
epi_date_type,
valid_run,
groupings,
env_variables_used,
report_dates
)
|
env_data |
Daily environmental data for the same groupfields and date
range as the epidemiological data. It may contain extra data (other
districts or date ranges). The data must be in long format (one row for each
date and environmental variable combination), and must start at absolutel
minimum |
quo_groupfield |
Quosure of the user given geographic grouping field to run_epidemia(). |
quo_obsfield |
Quosure of user given field name of the environmental data variables. |
quo_valuefield |
Quosure of user given field name of the value of the environmental data variable observations. |
env_ref_data |
Historical averages by week of year for environmental variables. Used in extended environmental data into the future for long forecast time, to calculate anomalies in early detection period, and to display on timeseries in reports. |
env_info |
Lookup table for environmental data - reference creation method (e.g. sum or mean), report labels, etc. |
fc_model_family |
The |
epi_date_type |
Extract from 'report_settings$epi_date_type' |
valid_run |
Internal TRUE/FALSE for whether this is part of a validation run. |
groupings |
A unique list of the geographic groupings (from groupfield). |
env_variables_used |
List of environmental variables that were used in the modeling (in 'report_settings$env_var' & found in env_data and env_info) |
report_dates |
Internally generated set of report date information: min, max, list of dates for full report, known epidemiological data period, forecast period, and early detection period. |
Environmental dataset, with data extended into the future forecast period. Unknown environmental data with runs of < 2 weeks is filled in with last known data (i.e. "persistence" method, using the mean of the previous week of known data). For missing data runs more than 2 weeks, the values are filled in using a progressive blend of the the mean of the last known week and the historical means.
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