calculate_coverage: Calculate coverage across predictive draws
In njhenry/covidemr: Tools for Mapping COVID Excess Mortality

Description Usage Arguments Value

Calculate how often observed data falls within X intervals of the posterior predictive draws. The observed data should come in the form of separate numerator and denominator columns, while the estimates should be a single field estimating a rate (i.e. already normalized by denominator)

calculate_coverage(
  in_data,
  num_field,
  denom_field,
  draw_fields,
  coverage_levels = c(0.5, 0.8, 0.95),
  group_fields = NULL,
  pois_sim = TRUE
)

`in_data`	Input data.table
`num_field`	Numerator field for the observed data
`denom_field`	Denominator field for the observed data
`draw_fields`	Character vector of fields containing predictive draws, in rate space (e.g. mortality rates)
`coverage_levels`	[optional, default c(.5, .8, .95)] Uncertainty intervals to calculate from the posterior predictive draws
`group_fields`	[optional, default NULL] If the predictive validity metrics should be grouped, list the fields to group them by here. If NULL (the default), the predictive validity metrics will be calculated across the entire dataset
`pois_sim`	[optional, default TRUE] Should the coverage be estimated using realizations Poisson distribution centered at population * rate (as opposed to the central value, population * p)?