Posterior draws of the delay distribution parameters

Returns posterior draws of the parameters of the delay distribution in the long format used by tidybayes. The delay parameters are the distributional parameters of the brms family, evaluated on the response scale for each row of newdata. For a lognormal model they are mu and sigma. They are the parameters of the delay distribution itself, so they do not describe the censoring or truncation of the observation process, and they are not the natural scale mean and standard deviation of the delay. Use add_summaries() to add those.

add_delay_parameter_draws() is the same function with newdata first, for use at the start of a pipeline as with tidybayes::add_epred_draws().

Usage

delay_parameter_draws(object, newdata = NULL, ...)

add_delay_parameter_draws(newdata, object, ...)

Arguments

object

A model fit with epidist().

newdata

A data.frame of data to predict for. If NULL, the default, the data the model was fitted to is used. The brms models epidist fits need the model variables as well as the predictors, so build newdata with epidist_strata() rather than from the predictors alone.

...

Additional arguments passed to brms::prepare_predictions(), such as ndraws or re_formula.

Value

A tibble of posterior draws of the delay distribution parameters, grouped by the columns of newdata and by .row. It has the epidist_delay_draws class, which records the delay distribution family and gives it a plot() method.

Details

The returned columns follow the tidybayes conventions. The columns of newdata come first, followed by .row, .chain, .iteration and .draw, followed by one column per distributional parameter. The result is grouped by the columns of newdata and by .row. .chain and .iteration are NA when the draws have been subset, because the chain a subset draw came from is not recoverable.

Every row of newdata gets its own draws, so passing the data the model was fitted to produces many identical draws when the model has few unique combinations of predictors. epidist_strata() returns one row per unique combination and is usually the better input.

See also

add_summaries() to add natural scale summaries of the delay, epidist_strata() to build newdata, and plot.epidist_delay_draws() to plot the draws.

Other postprocess: add_summaries(), delay_summary_draws(), epidist_delay_draws, epidist_strata()

Examples

# \donttest{
fit <- sierra_leone_ebola_data |>
  as_epidist_linelist_data(
    pdate_lwr = "date_of_symptom_onset",
    sdate_lwr = "date_of_sample_tested"
  ) |>
  as_epidist_aggregate_data() |>
  as_epidist_marginal_model() |>
  epidist(chains = 2, cores = 2, refresh = ifelse(interactive(), 250, 0))
#> 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 2394 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.
#> Warning: Found infinite values in the data, which may cause issues for Stan.
#> ℹ Data summarised by unique combinations of:
#> * Model variables: delay bounds, observation time, and primary censoring window
#> ! Reduced from 2453 to 272 rows.
#> ℹ This should improve model efficiency with no loss of information.
#> Warning: Found infinite values in the data, which may cause issues for Stan.
#> Warning: Found infinite values in the data, which may cause issues for Stan.
#> Compiling Stan program...
#> Start sampling

fit |>
  epidist_strata() |>
  add_delay_parameter_draws(fit) |>
  add_summaries(probs = c(0.05, 0.95))
#> # A tibble: 2,000 × 17
#> # Groups:   delay_lwr, relative_obs_time, pwindow, swindow, delay_upr,
#> #   delay_min, n, .row [1]
#>    delay_lwr relative_obs_time pwindow swindow delay_upr delay_min     n  .row
#>        <dbl>             <dbl>   <dbl>   <dbl>     <dbl>     <dbl> <int> <int>
#>  1         5               Inf       1       1         6         0    50     1
#>  2         5               Inf       1       1         6         0    50     1
#>  3         5               Inf       1       1         6         0    50     1
#>  4         5               Inf       1       1         6         0    50     1
#>  5         5               Inf       1       1         6         0    50     1
#>  6         5               Inf       1       1         6         0    50     1
#>  7         5               Inf       1       1         6         0    50     1
#>  8         5               Inf       1       1         6         0    50     1
#>  9         5               Inf       1       1         6         0    50     1
#> 10         5               Inf       1       1         6         0    50     1
#> # ℹ 1,990 more rows
#> # ℹ 9 more variables: .chain <int>, .iteration <int>, .draw <int>, mu <dbl>,
#> #   sigma <dbl>, mean <dbl>, sd <dbl>, q5 <dbl>, q95 <dbl>
# }