The meta model method for epidist_linelist_data objects

Prepares individual level data exactly as as_epidist_marginal_model.epidist_linelist_data() does and then stacks it with any supplied summary estimates.

Usage

# S3 method for class 'epidist_linelist_data'
as_epidist_meta_model(
  data,
  estimates = NULL,
  obs_time_threshold = 2,
  weight = NULL,
  delay_min = NULL,
  primary = .primary_choices(),
  ...
)

Arguments

data

An epidist_linelist_data object.

estimates

An epidist_estimates_data object of published summary estimates, or NULL.

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.

weight

A column name containing counts of identical linelist items. When specified, the user is declaring that rows with the same values represent the same observation occurring multiple times. This allows for efficient data representation by storing unique patterns with their counts rather than repeating identical rows. The marginal model will further aggregate these counts based on the formula specification. Default is NULL, which assigns a count of 1 to each row. Internally this is used to define the 'n' column of the returned object.

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 for the individual level rows, as in as_epidist_marginal_model.epidist_linelist_data(). "uniform", the default, assumes it is equally likely at any point. "expgrowth" tilts it, with the growth rate estimated as the pgrowth distributional parameter. Summary rows are unaffected. They tilt the primary event by the growth_rate metadata of their study, which as_epidist_estimates_data() takes as a known quantity.

...

Additional arguments passed to methods.

See also

Other meta_model: as_epidist_meta_model(), as_epidist_meta_model.NULL(), as_epidist_meta_model.epidist_aggregate_data(), as_epidist_meta_model.epidist_estimates_data(), assert_epidist.epidist_meta_model(), epidist_family_model.epidist_meta_model(), epidist_formula_model.epidist_meta_model(), epidist_model_prior.epidist_meta_model(), epidist_newdata.epidist_meta_model(), epidist_transform_data_model.epidist_meta_model(), is_epidist_meta_model(), new_epidist_meta_model()

Examples

sierra_leone_ebola_data |>
  as_epidist_linelist_data(
    pdate_lwr = "date_of_symptom_onset",
    sdate_lwr = "date_of_sample_tested"
  ) |>
  as_epidist_meta_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 8294 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: 8,358 × 35
#>    delay_lwr     n obs_type study_n trunc_adjusted trunc_design cens_adjusted
#>        <int> <dbl>    <int>   <int>          <int>        <int>         <int>
#>  1         5     1        1       0              0            0             0
#>  2         5     1        1       0              0            0             0
#>  3         5     1        1       0              0            0             0
#>  4         5     1        1       0              0            0             0
#>  5         5     1        1       0              0            0             0
#>  6         5     1        1       0              0            0             0
#>  7         5     1        1       0              0            0             0
#>  8         5     1        1       0              0            0             0
#>  9         5     1        1       0              0            0             0
#> 10         5     1        1       0              0            0             0
#> # ℹ 8,348 more rows
#> # ℹ 28 more variables: group_start <int>, group_len <int>, chol_start <int>,
#> #   n_quad <int>, relative_obs_time <dbl>, pwindow <dbl>, swindow <dbl>,
#> #   delay_upr <dbl>, delay_min <dbl>, report_se <dbl>, quantile_p <dbl>,
#> #   growth_rate <dbl>, ptime_lwr <dbl>, ptime_upr <dbl>, stime_lwr <dbl>,
#> #   stime_upr <dbl>, obs_time <dbl>, id <int>, age <dbl>, sex <chr>,
#> #   pdate_lwr <date>, sdate_lwr <date>, district <chr>, chiefdom <chr>, …