The naive model method for epidist_linelist_data objects

This method converts linelist data to a naive model format by calculating delays between primary and secondary events to enable model fitting in epidist(). If the input data contains an n column (e.g. from aggregated data), the likelihood will be weighted by these counts.

Usage

# S3 method for class 'epidist_linelist_data'
as_epidist_naive_model(data, weight = NULL, ...)

Arguments

data

An epidist_linelist_data object.

weight

A column name to use for weighting the data in the likelihood. Default is NULL. Internally this is used to define the 'n' column of the returned object.

...

Additional arguments passed to methods.

Details

When a formula is specified in epidist(), the data will be transformed using epidist_transform_data_model.epidist_naive_model() to prepare it for model fitting. This transformation summarises the data by counting unique combinations of delays and any variables in the model formula.

The naive model is the simplest approach but ignores censoring and truncation in the data by using only lower bounds as point estimates. For data with substantial censoring or truncation, consider using as_epidist_latent_model() or as_epidist_marginal_model() which properly account for these features.

See also

Other naive_model: as_epidist_naive_model(), as_epidist_naive_model.epidist_aggregate_data(), epidist_formula_model.epidist_naive_model(), epidist_transform_data_model.epidist_naive_model(), is_epidist_naive_model(), new_epidist_naive_model()

Examples

sierra_leone_ebola_data |>
  as_epidist_linelist_data(
    pdate_lwr = "date_of_symptom_onset",
    sdate_lwr = "date_of_sample_tested"
  ) |>
  as_epidist_naive_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).
#> # A tibble: 8,358 × 17
#>    ptime_lwr ptime_upr stime_lwr stime_upr obs_time    id   age sex   pdate_lwr 
#>        <dbl>     <dbl>     <dbl>     <dbl>    <dbl> <int> <dbl> <chr> <date>    
#>  1         0         1         5         6      484     1    20 Fema… 2014-05-18
#>  2         2         3         7         8      484     2    42 Fema… 2014-05-20
#>  3         2         3         7         8      484     3    45 Fema… 2014-05-20
#>  4         3         4         8         9      484     4    15 Fema… 2014-05-21
#>  5         3         4         8         9      484     5    19 Fema… 2014-05-21
#>  6         3         4         8         9      484     6    55 Fema… 2014-05-21
#>  7         3         4         8         9      484     7    50 Fema… 2014-05-21
#>  8         4         5         9        10      484     8     8 Fema… 2014-05-22
#>  9         4         5         9        10      484     9    54 Fema… 2014-05-22
#> 10         4         5         9        10      484    10    57 Fema… 2014-05-22
#> # ℹ 8,348 more rows
#> # ℹ 8 more variables: sdate_lwr <date>, district <chr>, chiefdom <chr>,
#> #   pdate_upr <date>, sdate_upr <date>, obs_date <date>, delay <dbl>, n <dbl>