Skip to contents

Canonicalize Observation Data

Source: R/imuGAP.R
canonicalize_observations.Rd

Canonicalize Observation Data

Usage

canonicalize_observations(observations, drop_extra = TRUE)

Arguments

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

drop_extra

a logical scalar; drop extraneous columns? (default: yes)

Value

a canonical observation object, a [data.table()] with:

  • an obs_c_id column, an integer sequence from 1; the order observations will be passed to estimation

  • the original obs_id column, possibly reordered

  • positive and sample_n columns, possibly reordered

  • a "censored" column; all NA, if not present in original observations argument

Details

The observations object documents observations used to fit the model. Conceptually, each row represents an observation of vaccination status within a population. That population need not be uniform (see [canonicalize_populations()]) or concerning a single cohort or time: each observation should generally be the best available resolution data. That resolution can vary across rows. The [imuGAP()] sampler uses information about the resolutions to automatically figure out how to compare the latent process model to those different observations.

For the optional censored column: the model supports vaccination status indicators which are vaccine specific as well as those which represent an individual having all of a set of vaccines (including the target vaccine). The specific coverage for the target vaccine is right-censored in the latter case: full-set-coverage is the minimum coverage for the target.

When at least some of the data are censored, you must supply the censored column to correctly estimate coverage. Mark any uncensored observations with NA, and any right-censored observations with $1$. Note that $0$ is not a valid value at this time; we are preserving that for potential future support of left-censoring.