Build newdata for prediction from an epidist data object

Prediction with brms and tidybayes needs a newdata argument holding every variable the model uses. As well as the variables in your formula that means the response and the observation process variables that epidist adds. Their names depend on the model, so building newdata by hand means knowing how each model is specified. This function builds it for you. It expands the variables you supply into a grid, in the same way as tidyr::expand(), and adds the rest with values you set through named arguments.

Usage

epidist_newdata(data, ...)

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.

Value

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

Details

The defaults give the delay distribution with no censoring and no truncation. Set pwindow and swindow to ask for censoring, and relative_obs_time to ask for truncation. See the method for each model for the arguments it takes.

The result is a plain tibble::tibble(). Pass it to brms::posterior_epred(), brms::posterior_predict(), predict_delay_parameters(), or to the tidybayes functions add_epred_draws() and add_predicted_draws().

See also

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

# An intercept only model
epidist_newdata(prep_obs)
#> # A tibble: 1 × 6
#>   delay_lwr relative_obs_time pwindow swindow delay_upr delay_min
#>       <dbl>             <dbl>   <dbl>   <dbl>     <dbl>     <dbl>
#> 1         0               Inf       0       0         0         0

# A row for each sex
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