Fit epidemiological delay distributions using a brms interface

Fit epidemiological delay distributions using a brms interface

Usage

epidist(
  data,
  formula = mu ~ 1,
  family = lognormal(),
  prior = NULL,
  merge_priors = TRUE,
  fn = brms::brm,
  ...
)

Arguments

data: An object with class corresponding to an implemented model.
formula: An object of class stats::formula or brms::brmsformula (or one that can be coerced to those classes). A symbolic description of the model to be fitted. A formula must be provided for the distributional parameter mu, and may optionally be provided for other distributional parameters.
family: A description of the response distribution and link function to be used in the model. Every family function has a link argument allowing users to specify the link function to be applied on the response variable. If not specified, default links are used. For details of all supported families see brms::brmsfamily(). Commonly used, such as brms::lognormal(), are also reexported as part of epidist.
prior: One or more brmsprior objects created by brms::set_prior() or related functions. These priors are passed to epidist_prior() in the prior argument. Some models have default priors that are automatically added (see epidist_model_prior()). These can be merged with user-provided priors using the merge_priors argument.
merge_priors: If TRUE then merge user priors with default priors, if FALSE only use user priors. Defaults to TRUE. This may be useful if the built in approaches for merging priors are not flexible enough for a particular use case.
fn: The internal function to be called. By default this is brms::brm() which performs inference for the specified model. Other options are brms::make_stancode() which returns the Stan code for the specified model, or brms::make_standata() which returns the data passed to Stan. These two later options may be useful for model debugging and extensions.
...: Additional arguments passed to fn method.

Value

A fitted model of class epidist_fit.

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

summary(fit)
#>  Family: marginal_lognormal 
#>   Links: mu = identity; sigma = log 
#> Formula: delay_lwr | weights(n) + vreal(relative_obs_time, pwindow, swindow, delay_upr, delay_min) ~ 1 
#>          sigma ~ 1
#>    Data: transformed_data (Number of observations: 272) 
#>   Draws: 2 chains, each with iter = 2000; warmup = 1000; thin = 1;
#>          total post-warmup draws = 2000
#> 
#> Regression Coefficients:
#>                 Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
#> Intercept           1.62      0.01     1.60     1.63 1.00     2031     1369
#> sigma_Intercept    -0.53      0.01    -0.54    -0.51 1.00     2219     1370
#> 
#> Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
#> and Tail_ESS are effective sample size measures, and Rhat is the potential
#> scale reduction factor on split chains (at convergence, Rhat = 1).
# }