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, delay_min = NULL, ...)

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.

delay_min

Minimum delay (left truncation point). Can be:

• NULL (default): uses a delay_min column from the data if present, otherwise defaults to 0 (no left truncation).

• A numeric scalar: applied to all observations.

• A column name string: looks up the named column in the data. This is passed as the L parameter to primarycensored::dpcens().

...

Not used in this method.

Value

An object of class epidist_marginal_model.

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_newdata.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 relative observation times (`relative_obs_time`) greater than 98
#>   (2x the maximum delay) to Inf.
#> ℹ This improves model efficiency by reducing the number of unique observation
#>   times in the data.
#> ℹ The impact on model accuracy should be negligible because these relative
#>   observation times are high enough to cause very limited right truncation.
#> ℹ The original relative observation times are available in
#>   `orig_relative_obs_time`.
#> ℹ Raise `obs_time_threshold` to avoid this behaviour.
#> # A tibble: 2,453 × 18
#>    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 10 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>,
#> #   delay_min <dbl>