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The transport discriminant of a reporting series

Source: R/transport_discriminant.R
transport_discriminant.Rd

[Experimental]

Computes, for every report date, the two coordinates of diagnose_batches()'s conservation law – the deficit (the transport axis: how many reports the preceding window is missing) and the window discriminant (the creation axis: the window total relative to its baseline) – together with their robust standardised versions transport_z and creation_z.

Usage

transport_discriminant(
  x,
  lookback = 7L,
  baseline_window = NULL,
  period = NULL,
  alpha = 0.05,
  axis = c("report", "revision"),
  drop_censored = TRUE
)

Arguments

x

A tbl_now() object.

lookback

Integer window half-width k (report-grid steps) over which the deficit is accumulated. Default 7 (a week of daily reporting).

baseline_window, period

Baseline controls, passed through to the same machinery as diagnose_batches(). period (e.g. 7) absorbs a scheduled weekly reporting cadence.

alpha

Level for the classification labels. Default 0.05.

axis

Which time axis to scan for arrivals: "report" (default) or "revision". Needs a revision process (see add_revision_date()); cases still "pending" are left out.

drop_censored

Logical. Ignore the rows whose date on axis is flagged censored (is_censored_report, or is_censored_revision on the revision axis). Default TRUE: a censored date is a bound, not the date the record arrived, so those rows would pile up on the censoring date and be rediscovered as the very batch the censoring already recorded.

Value

A tibble of class transport_discriminant, one row per (report date, stratum), with columns report_date, stratum, reported, baseline, window_total, spike (reported minus baseline), deficit, delta, transport_z, creation_z, classification and batch.

Details

A batch moves reports later without creating them, so it leaves a positive deficit while conserving the window total (transport_z large, creation_z near 0). A genuine surge creates reports, lifting the window total without a deficit (creation_z large, transport_z near 0). Reading the two together separates a backlog release from an epidemic surge: a point sits in the batch corner when its transport score is large and its creation score is not. A negative creation_z with no transport is a hold in progress (the window is depleted and nothing has been released yet). The classification column applies these labels at level alpha, exactly as in diagnose_batches().

See also

diagnose_batches() for the hypothesis test, diagnostic_plot() to plot this plane.

Examples

data(denguedat)
# The two and a half years around the 1996 and 1997 backlog dumps. The whole
# twenty-year series works the same way, it just takes longer to scan.
window <- denguedat[
  denguedat$onset_week >= as.Date("1995-06-01") &
    denguedat$onset_week <= as.Date("1998-01-01"),
]
dn <- tbl_now(window, onset_week, report_week, verbose = FALSE)
td <- transport_discriminant(dn)
td[td$batch, ]
#> <transport_discriminant>: 2 report dates, look-back 7, 2 batches and 0 surges at alpha = 0.05.
#> # A tibble: 2 × 14
#>   report_date stratum reported baseline window_total spike deficit delta
#>   <date>      <chr>      <dbl>    <dbl>        <dbl> <dbl>   <dbl> <dbl>
#> 1 1996-02-12  all           46     29.4          236  16.6    60.7 -44.1
#> 2 1997-09-15  all           93     55.7          330  37.3    92   -54.7
#> # ℹ 6 more variables: transport_z <dbl>, creation_z <dbl>, p_transport <dbl>,
#> #   p_creation <dbl>, classification <chr>, batch <lgl>