The marginal model method for epidist_aggregate_data objects — as_epidist_marginal_model.epidist_aggregate

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,
  primary = .primary_choices(),
  ...
)

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().

primary

The distribution of the primary event within its censoring window. "uniform", the default, assumes it is equally likely at any point. "expgrowth" tilts it, with the growth rate estimated as the pgrowth distributional parameter.

...

Not used in this method.

Value

An object of class 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>

The marginal model method for `epidist_aggregate_data` objects

Usage

Arguments

Value

See also

Examples