Posterior draws of the delay distribution, summarised

The usual last step of a fit. Builds one row per unique combination of the predictors with epidist_strata(), draws the delay distribution parameters for each with delay_parameter_draws(), and adds the natural scale mean and standard deviation, and any quantiles asked for, with add_summaries().

Each of those three steps is available on its own where a step needs different arguments, or where newdata is built some other way.

Usage

delay_summary_draws(
  object,
  newdata = NULL,
  vars = NULL,
  probs = NULL,
  method = c("auto", "analytic", "sample"),
  nsim = 1000,
  ...
)

Arguments

object

A model fit with epidist().

newdata

A data.frame of data to predict for. If NULL, the default, epidist_strata() builds one row per unique combination of the predictors the model uses.

vars

A character vector of variables to stratify by, passed to epidist_strata(). Only used when newdata is NULL.

probs

A numeric vector of probabilities to add quantiles of the delay distribution for. If NULL, the default, no quantiles are added.

method

Passed to add_summaries(). Either "auto", the default, which uses the analytic solution when there is one and simulates otherwise, "analytic", or "sample".

nsim

The number of delays to simulate per row. Passed to add_summaries() and only used when simulating.

...

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

Value

A tibble of posterior draws of the delay distribution parameters with mean and sd columns added, and one column per element of probs. Grouped as delay_parameter_draws() returns it.

See also

delay_parameter_draws() for the parameters alone, add_summaries() to summarise draws you already have, and epidist_strata() to build newdata.

Other postprocess: add_summaries(), delay_parameter_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

delay_summary_draws(fit, probs = c(0.05, 0.95))
#> Warning: Found infinite values in the data, which may cause issues for Stan.
#> # 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>
# }