The retro-score is the amount of retro-adjustments / max possible retro-adjustments The higher the better for nowcast_cl() retro_score = n_changes / max_changes or = retro_adjustments / max_retro_adj Notes: "retro-adjustments" = "value changes" retro-score = number of changes / number of ywks (max changes)
Usage
calculate_retro_score(
df,
col_date_occurrence,
col_date_reporting,
col_value,
group_cols = NULL,
method = "2D_allgroups",
max_delay = Inf,
aggrby
)Arguments
- df
A data.frame or tibble.
- col_date_occurrence
Column name for the date of occurrence/reference.
- col_date_reporting
Column name for the date of reporting.
- col_value
Column name for the value.
- group_cols
Optional character vector of column names for grouping.
- method
'2D_allgroups' (number of changes in 2D triangle) or 'at_least_1_change_by_occ' (number of occurrence dates with at least 2 reported values)
- max_delay
Maximum delay to consider. (only works with method '2D_allgroups')
- aggrby
A character vector of column names to aggregate by.
Examples
generate_test_data() %>%
calculate_retro_score(
col_date_occurrence = date_occurrence,
col_date_reporting = date_report,
col_value = value,
group_cols = NULL
# , aggrby = country
# , method = "at_least_1_change_by_occ"
)
#> # A tibble: 1 × 3
#> n_changes max_retro_adj retro_score
#> <dbl> <dbl> <dbl>
#> 1 72 72 1