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Summarise the posterior tidily

Source: R/postprocess.R
fv_tidy_posterior.Rd

A very opinionated wrapper around posterior::summarise_draws() with cleaning and tidying including linking to observed data, tidying parameter names, and transforming parameters for interpretability. See fv_posterior() for a more generic solution.

Usage

fv_tidy_posterior(
  fit,
  probs = c(0.05, 0.2, 0.8, 0.95),
  digits = 3,
  voc_label = "VOC",
  scale_r = 1,
  timespan = 7
)

Arguments

fit

List of output as returned by fv_sample().

probs

A vector of numeric probabilities to produce quantile summaries for. By default these are the 5%, 20%, 80%, and 95% quantiles which are also the minimum set required for plotting functions to work (such as plot_cases(), plot_rt(), and plot_voc_frac()).

digits

Numeric, defaults to 3. Number of digits to round summary statistics to.

voc_label

A character string, default to "VOC". Defines the label to assign to variant of concern specific parameters. Example usage is to rename parameters to use variant specific terminology.

scale_r

Numeric, defaults to 1. Rescale the timespan over which the growth rate and reproduction number is calculated. An example use case is rescaling the growth rate from weekly to be scaled by the mean of the generation time (for COVID-19 for example this would be 5.5 / 7.

timespan

Integer, defaults to 7. Indicates the number of days between each observation. Defaults to a week.

Value

A dataframe summarising the model posterior. Output is stratified by value_type with posterior summaries by case, voc, voc advantage vs non-voc over time, rt, growth, model, and the raw posterior summary.

See also

Functions used for postprocessing of model fits convert_to_stanfit(), extract_draws(), extract_forecast_dates(), fv_extract_forecast(), fv_posterior(), link_dates_with_posterior(), link_obs_with_posterior(), plot.fv_posterior(), print.fv_posterior(), quantiles_to_long(), summary.fv_posterior(), update_voc_label()

Examples

if (FALSE) { # interactive()
options(mc.cores = 4)
obs <- filter_by_availability(
  germany_covid19_delta_obs,
  date = as.Date("2021-06-12"),
)
dt <- fv_as_data_list(obs)
inits <- fv_inits(dt)
fit <- fv_sample(dt, init = inits, adapt_delta = 0.99, max_treedepth = 15)
fv_tidy_posterior(fit)
}