The transport discriminant of a reporting series

Usage

transport_discriminant(
  data,
  lookback = 7L,
  baseline_window = NULL,
  period = NULL,
  alpha = 0.05
)

Arguments

data

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 batch_test(). period (e.g. 7) absorbs a scheduled weekly reporting cadence.

alpha

Level for the classification labels. Default 0.05.

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

Computes, for every report date, the two coordinates of batch_test()'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.

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 batch_test().

See also

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

Examples

data(denguedat)
dn <- tbl_now(denguedat, onset_week, report_week, verbose = FALSE)
td <- transport_discriminant(dn)
td[td$batch, ]
#> # A tibble: 3 × 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    92.0 -54.7
#> 3 2006-07-31  all           27     13.9           67  13.1    40.5 -27.4
#> # ℹ 6 more variables: transport_z <dbl>, creation_z <dbl>, p_transport <dbl>,
#> #   p_creation <dbl>, classification <chr>, batch <lgl>