The marginal model method for epidist_aggregate_data objects

This method converts aggregate data to a marginal model format by passing it to as_epidist_marginal_model.epidist_linelist_data() with the n column used as weights. This ensures that the likelihood is weighted by the counts in the aggregate data.

Usage

# S3 method for class 'epidist_aggregate_data'
as_epidist_marginal_model(data, obs_time_threshold = 2, ...)

Arguments

data

An epidist_aggregate_data object

obs_time_threshold

Ratio used to determine threshold for setting relative observation times to Inf. Observation times greater than obs_time_threshold times the maximum delay will be set to Inf to improve model efficiency by reducing the number of unique observation times. Default is 2.

...

Not used in this method.

See also

Other marginal_model: as_epidist_marginal_model(), as_epidist_marginal_model.epidist_linelist_data(), epidist_family_model.epidist_marginal_model(), epidist_formula_model.epidist_marginal_model(), epidist_transform_data_model.epidist_marginal_model(), is_epidist_marginal_model(), new_epidist_marginal_model()

Examples

sierra_leone_ebola_data |>
  dplyr::count(date_of_symptom_onset, date_of_sample_tested) |>
  as_epidist_aggregate_data(
    pdate_lwr = "date_of_symptom_onset",
    sdate_lwr = "date_of_sample_tested",
    n = "n"
  ) |>
  as_epidist_marginal_model()
#> ℹ No primary event upper bound provided, using the primary event lower bound + 1 day as the assumed upper bound.
#> ℹ No secondary event upper bound provided, using the secondary event lower bound + 1 day as the assumed upper bound.
#> ℹ No observation time column provided, using 2015-09-14 as the observation date (the maximum of the secondary event upper bound).
#> ! Setting 2394 observation times beyond 98 (=2x max delay) to Inf. This
#>   improves model efficiency by reducing unique observation times while
#>   maintaining model accuracy as these times should have negligible impact.
#> # A tibble: 2,453 × 17
#>    ptime_lwr ptime_upr stime_lwr stime_upr obs_time pdate_lwr  sdate_lwr      n
#>        <dbl>     <dbl>     <dbl>     <dbl>    <dbl> <date>     <date>     <int>
#>  1         0         1         5         6      484 2014-05-18 2014-05-23     1
#>  2         2         3         7         8      484 2014-05-20 2014-05-25     2
#>  3         3         4         8         9      484 2014-05-21 2014-05-26     4
#>  4         4         5         9        10      484 2014-05-22 2014-05-27     6
#>  5         8         9        13        14      484 2014-05-26 2014-05-31     1
#>  6         9        10        14        15      484 2014-05-27 2014-06-01     3
#>  7        11        12        16        17      484 2014-05-29 2014-06-03     7
#>  8        12        13        17        18      484 2014-05-30 2014-06-04     7
#>  9        13        14        18        19      484 2014-05-31 2014-06-05     1
#> 10        13        14        20        21      484 2014-05-31 2014-06-07     1
#> # ℹ 2,443 more rows
#> # ℹ 9 more variables: pdate_upr <date>, sdate_upr <date>, obs_date <date>,
#> #   pwindow <dbl>, swindow <dbl>, relative_obs_time <dbl>,
#> #   orig_relative_obs_time <dbl>, delay_lwr <dbl>, delay_upr <dbl>