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Check observation meta data object

Source: R/imuGAP.R
canonicalize_populations.Rd

Check observation meta data object

Usage

canonicalize_populations(
  populations,
  observations,
  locations,
  max_cohort,
  max_age,
  max_dose = 2L
)

Arguments

populations

a [data.frame()], the the observation meta data, with columns

  • obs_id, any type; the observation the row concerns (i.e. id shared with an observations data object)

  • `loc_id“, any type; the location the row concerns (i.e. id shared with a locations data object)

  • dose, a non-zero, positive integer (1, 2, ...); what dose row concerns

  • cohort, a positive integer; the cohort at the location row concerns

  • age, a positive integer; the age of that cohort row concerns

  • weight, a numeric, (0, 1); the relative contribution of this row to an observation Note that multiple rows may concern the same observation, meaning that the populations from different cohorts, locations, and ages may be pooled in an observation

observations

a [data.frame()], the observed data, with at least three columns:

  • an obs_id column; any type, as long as unique, non-NA

  • a `positive“ column; non-negative integers, the observed number of vaccinated individuals

  • a sample_n column; positive integers, the number of individuals sampled, must be greater than or equal to "positive"

  • optionally, a censored column; numeric, NA (uncensored) or 1 (right-censored); if not present, will be assumed NA

locations

a [data.frame()], with columns loc_id and parent_id, of the same type. See Details for restrictions.

max_cohort

if present, what is the maximum cohort that should be present?

max_age

if present, what is the maximum age that should be present?

Value

a canonical populations object, mirroring the input populations, with the following updates:

  • obs_c_id, the observation id the row concerns, canonicalized to match the canonical observation ids

  • loc_c_id, the location id the row concerns, canonicalized to match

  • reordered to obs_c_id order

Details

This method validates the meta-data associated with the observations, as well as converting that meta-data to use the canonical id formats.

Regarding "cohorts" and "ages": these are counted from 1, by 1 "unit". You can imagine the units are whatever resolution is appropriate for your data: months, quarters, years, etc. As long as these are used consistently, estimation will work, and take on the unit meaning you used for input.