Categorises incidence by delay group with empirical
reporting proportions. Intended for use with the
plot.enw_preprocess_data() visualisation types that show
reporting patterns by delay.
Arguments
- pobs
A preprocessed data object as produced by
enw_preprocess_data().- delay_group_thresh
A numeric vector defining left-closed interval thresholds for grouping reporting delays. The smallest value should be zero and the largest should exceed
max_delay(intervals are left-closed, right-open). Delays outside the range are dropped.
Value
A data.table of notification incidence by reference
date and delay group, including columns prop_reported
and cum_prop_reported.
See also
Plotting functions
enw_delay_quantiles(),
enw_plot_delay_counts(),
enw_plot_delay_cumulative(),
enw_plot_delay_fraction(),
enw_plot_delay_quantiles(),
enw_plot_nowcast_quantiles(),
enw_plot_obs(),
enw_plot_pp_quantiles(),
enw_plot_quantiles(),
enw_plot_theme(),
plot.enw_preprocess_data(),
plot.epinowcast()
Examples
pobs <- enw_example("preprocessed_observations")
enw_delay_categories(pobs, delay_group_thresh = c(0, 2, 5, 10, 21))
#> Key: <.group>
#> .group reference_date delay_group confirm new_confirm max_confirm
#> <num> <IDat> <fctr> <int> <int> <int>
#> 1: 1 2021-07-14 [0,2) 34 34 70
#> 2: 1 2021-07-14 [2,5) 43 9 70
#> 3: 1 2021-07-14 [5,10) 64 21 70
#> 4: 1 2021-07-14 [10,21) 70 6 70
#> 5: 1 2021-07-15 [0,2) 43 43 69
#> ---
#> 139: 1 2021-08-19 [2,5) 202 31 202
#> 140: 1 2021-08-20 [0,2) 159 159 171
#> 141: 1 2021-08-20 [2,5) 171 12 171
#> 142: 1 2021-08-21 [0,2) 112 112 112
#> 143: 1 2021-08-22 [0,2) 45 45 45
#> prop_reported cum_prop_reported
#> <num> <num>
#> 1: 0.48571429 0.4857143
#> 2: 0.12857143 0.6142857
#> 3: 0.30000000 0.9142857
#> 4: 0.08571429 1.0000000
#> 5: 0.62318841 0.6231884
#> ---
#> 139: 0.15346535 1.0000000
#> 140: 0.92982456 0.9298246
#> 141: 0.07017544 1.0000000
#> 142: 1.00000000 1.0000000
#> 143: 1.00000000 1.0000000