The abstract parent class for positivity diagnostics

positivity_diagnostic is the abstract S7 parent that every positively diagnostic result inherits from. It cannot be instantiated directly. It fixes the shared property set (a tidy results tibble, the exposure column names and type, the sample size, the resolved method parameters, and the originating call) and supplies the generics::tidy(), generics::glance(), summary(), and print() behavior a diagnostic inherits unless it overrides one. Package developers extend it when adding a new diagnostic.

Arguments

results

A tibble of tidy diagnostic output.

exposure

The exposure column name or names, time-ordered when the diagnostic is sequential.

exposure_type

One of "binary", "categorical", or "continuous".

n

The number of observations, an integer.

params

A list of method parameters as supplied and resolved.

call

The originating call.

Value

An abstract class object. Construction of a subclass returns a positivity_diagnostic.

Details

generics::tidy() returns @results unchanged. The inherited generics::glance() returns a one-row tibble holding the single column n, the sample size, because a subclass that adds no statistics of its own has nothing else to report. Each diagnostic overrides it to state its own statistics beside n, typed as they are computed.

summary() reports those statistics in long form, with the columns statistic, value, and threshold. Not every generics::glance() column earns a row. value is numeric throughout, so the character and logical statistics stay behind in the wide output, as do the sample size, the resolved method parameters, and any column that another statistic reports as its threshold, which would otherwise be stated twice. A diagnostic may override summary() to aggregate something else entirely: check_extrapolation() summarizes its per-unit results by exposure group instead.

threshold is the one number behind the row, stated in the units of the quantity it cuts, which are not always the units of value. Where the statistic is itself a reading, the cut applies to that reading, so phi_hat sits beside the null quantile it was compared against. Where the statistic counts how many rows crossed a cut, the cut applies to the per-row quantity rather than to the count, so a count of low-support subgroups sits beside a subgroup prevalence and a count of high-leverage candidates beside a hat value.

threshold is NA wherever there is no one number to state. That covers a statistic that is a raw magnitude, a statistic governed by several parameters with no single cut, a run that resolved a different cut at each wave rather than one throughout, and a statistic whose cut is real but is not one of the pairings the package tracks. An NA therefore reports that the summary has no cut to show, not that the diagnostic compared nothing against anything. The pairings are fixed inside positively; a diagnostic defined outside the package reports NA throughout.