Build newdata for the latent model

The latent model uses the response, the observation time relative to the primary event, and the primary and secondary censoring windows. This method adds all of them. The response is set to NA because it is the quantity being predicted. The rest are set here.

Usage

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

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.

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 latent_model: as_epidist_latent_model(), as_epidist_latent_model.epidist_aggregate_data(), as_epidist_latent_model.epidist_linelist_data(), epidist_family_model.epidist_latent_model(), epidist_formula_model.epidist_latent_model(), epidist_model_prior.epidist_latent_model(), is_epidist_latent_model(), new_epidist_latent_model()

Other newdata: epidist_newdata(), epidist_newdata.default(), epidist_newdata.epidist_marginal_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_latent_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 row for each sex, with no censoring and no truncation
epidist_newdata(prep_obs, sex)
#> # A tibble: 3 × 5
#>   sex    delay relative_obs_time pwindow swindow
#>   <chr>  <dbl>             <dbl>   <dbl>   <dbl>
#> 1 Female    NA               Inf       0       0
#> 2 Male      NA               Inf       0       0
#> 3 NA        NA               Inf       0       0

# The same, with daily censoring
epidist_newdata(prep_obs, sex, pwindow = 1, swindow = 1)
#> # A tibble: 3 × 5
#>   sex    delay relative_obs_time pwindow swindow
#>   <chr>  <dbl>             <dbl>   <dbl>   <dbl>
#> 1 Female    NA               Inf       1       1
#> 2 Male      NA               Inf       1       1
#> 3 NA        NA               Inf       1       1