Calculate delay metadata based on the supplied maximum delay and independent
of other metadata or date indexing. These data are meant to be used in
conjunction with metadata on the date of reference. Users can build
additional features this data.frame or regenerate it using this function
in the output of enw_preprocess_data().
Arguments
- max_delay
Numeric defaults to 20 and needs to be greater than or equal to 1 and an integer (internally it will be coerced to one using
as.integer()). The maximum number of days to include in the delay distribution. Computation scales non-linearly with this setting so consider what maximum makes sense for your data carefully. Note that this is zero indexed and so includes the reference date andmax_delay - 1other days (i.e. amax_delayof 1 corresponds with no delay). If amax_delaygreater than the maximum delay in the data is supplied thenenw_preprocess_data()will throw a warning but in some cases this may be appropriate (e.g. if at the beginning of a time series). In these cases the user should check the model specification carefully as the model will be extrapolating beyond the observed data.- breaks
Numeric, defaults to 4. The number of breaks to use when constructing a categorised version of numeric delays.
- timestep
The timestep to used. This can be a string ("day", "week", "month") or a numeric whole number representing the number of days.
Value
A data.frame of delay metadata. This includes:
delay: The numeric delay from reference date to report.delay_cat: The categorised delay. This may be useful for model building.delay_week: The numeric week since the delay was reported. This again may be useful for model building.delay_head: A logical variable defining if the delay is in the lower 25% of the potential delays. This may be particularly useful when building models that assume a parametric distribution in order to increase the weight of the head of the reporting distribution in a pragmatic way.delay_tail: A logical variable defining if the delay is in the upper 75% of the potential delays. This may be particularly useful when building models that assume a parametric distribution in order to increase the weight of the tail of the reporting distribution in a pragmatic way.
See also
Preprocessing functions
enw_add_delay(),
enw_add_max_reported(),
enw_add_metaobs_features(),
enw_assign_group(),
enw_complete_dates(),
enw_construct_data(),
enw_extend_date(),
enw_filter_delay(),
enw_filter_reference_dates(),
enw_filter_report_dates(),
enw_flag_observed_observations(),
enw_impute_na_observations(),
enw_latest_data(),
enw_metadata(),
enw_missing_reference(),
enw_preprocess_data(),
enw_reporting_triangle_to_long(),
enw_reporting_triangle()
Examples
enw_delay_metadata(20, breaks = 4)
#> delay delay_cat delay_week delay_head delay_tail
#> <int> <fctr> <int> <lgcl> <lgcl>
#> 1: 0 [0,5) 0 TRUE FALSE
#> 2: 1 [0,5) 0 TRUE FALSE
#> 3: 2 [0,5) 0 TRUE FALSE
#> 4: 3 [0,5) 0 TRUE FALSE
#> 5: 4 [0,5) 0 TRUE FALSE
#> 6: 5 [5,10) 0 FALSE FALSE
#> 7: 6 [5,10) 0 FALSE FALSE
#> 8: 7 [5,10) 1 FALSE FALSE
#> 9: 8 [5,10) 1 FALSE FALSE
#> 10: 9 [5,10) 1 FALSE FALSE
#> 11: 10 [10,15) 1 FALSE FALSE
#> 12: 11 [10,15) 1 FALSE FALSE
#> 13: 12 [10,15) 1 FALSE FALSE
#> 14: 13 [10,15) 1 FALSE FALSE
#> 15: 14 [10,15) 2 FALSE FALSE
#> 16: 15 [15,20) 2 FALSE TRUE
#> 17: 16 [15,20) 2 FALSE TRUE
#> 18: 17 [15,20) 2 FALSE TRUE
#> 19: 18 [15,20) 2 FALSE TRUE
#> 20: 19 [15,20) 2 FALSE TRUE
#> delay delay_cat delay_week delay_head delay_tail