Build newdata for the marginal model

The marginal model uses the response, the observation time relative to the primary event, the primary and secondary censoring windows, the upper bound of the delay and the minimum delay. This method adds all of them. The response and its upper bound are set to 0. brms needs the response in newdata and needs it to be a whole number for this model, but prediction ignores its value. The rest are set here.

Usage

# S3 method for class 'epidist_marginal_model'
epidist_newdata(
  data,
  ...,
  pwindow = 0,
  swindow = 0,
  relative_obs_time = Inf,
  delay_min = 0
)

Arguments

data

An epidist data object, such as one returned by as_epidist_marginal_model(), as_epidist_latent_model() or as_epidist_naive_model().

...

Variables to expand into a grid, passed to tidyr::expand(). Supply the variables used in the model formula, such as sex. Each combination of their unique values becomes a row. Supply no variables to get a single row, which is what an intercept only model needs. A variable expanded here keeps its expanded values, so naming it as an argument of the method as well is an error.

pwindow

Width of the primary event censoring window. Defaults to 0, which is no censoring.

swindow

Width of the secondary event censoring window. Defaults to 0, which is no censoring.

relative_obs_time

Observation time relative to the primary event. Defaults to Inf, which is no right truncation. brms warns about infinite values in the data when this is Inf. That warning is safe here, because prediction is done in R and never passes the value to Stan.

delay_min

Minimum delay, the left truncation point. Defaults to 0, which is no left truncation.

Value

A tibble::tibble() of newdata ready to predict from.

Details

The defaults give the delay distribution with no censoring and no truncation, which is a continuous probability density function. For a discrete probability mass function with daily censoring set pwindow and swindow to 1. For the delay distribution as it would be seen at a given time set relative_obs_time to that time relative to the primary event.

See also

Other marginal_model: as_epidist_marginal_model(), as_epidist_marginal_model.epidist_aggregate_data(), 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()

Other newdata: epidist_newdata(), epidist_newdata.default(), epidist_newdata.epidist_latent_model(), epidist_newdata.epidist_naive_model()

Examples

prep_obs <- sierra_leone_ebola_data |>
  as_epidist_linelist_data(
    pdate_lwr = "date_of_symptom_onset",
    sdate_lwr = "date_of_sample_tested"
  ) |>
  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 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 row for each sex, with no censoring and no truncation
epidist_newdata(prep_obs, sex)
#> # A tibble: 3 × 7
#>   sex    delay_lwr relative_obs_time pwindow swindow delay_upr delay_min
#>   <chr>      <dbl>             <dbl>   <dbl>   <dbl>     <dbl>     <dbl>
#> 1 Female         0               Inf       0       0         0         0
#> 2 Male           0               Inf       0       0         0         0
#> 3 NA             0               Inf       0       0         0         0

# The same, with daily censoring
epidist_newdata(prep_obs, sex, pwindow = 1, swindow = 1)
#> # A tibble: 3 × 7
#>   sex    delay_lwr relative_obs_time pwindow swindow delay_upr delay_min
#>   <chr>      <dbl>             <dbl>   <dbl>   <dbl>     <dbl>     <dbl>
#> 1 Female         0               Inf       1       1         0         0
#> 2 Male           0               Inf       1       1         0         0
#> 3 NA             0               Inf       1       1         0         0