Compute surprise scores for new observations — surprise • diseasenowcasting

Given a fitted nowcast and new data that arrived after the fit, compute how surprising the new data is under the fitted posterior.

Two types of surprise are supported (select via the type argument):

"count": How surprising is a new case count at a given event time
"delay": How surprising is a report with a very long delay
"both": Compute both (requires appropriate new_data format).

Usage

surprise(
  object,
  new_data,
  type = c("both", "count", "delay"),
  level = 0.99,
  n_draws = 500L,
  seed = sample.int(.Machine$integer.max, 1)
)

Arguments

object: A nowcast_class object (or fit() result).
new_data: For type = "count": a data.frame with columns event_index (0-indexed) and count (observed new total at that time). For type = "delay": a data.frame with columns delay (numeric delay values) and optionally weight (counts with that delay, default 1). For type = "both": supply both sets of columns.
type: Which surprise type(s) to compute. One of "count", "delay", "both" (default "both").
level: Credible level used to flag surprises (default 0.99). A count is flagged when it falls in the outer (1 - level)/2 tail of the posterior predictive (too high or too low); a delay is flagged when P(D >= d) or P(D <= d) is below 1 - level (surprisingly long or short).
n_draws: Number of posterior draws (default 500).
seed: Optional RNG seed.

Value

A list with:

$count_surprise (data.frame): one row per event time with event_index, observed, posterior_mean, lpd, ppp_right, ppp_left, relative_surprise.
$delay_surprise (data.frame): one row per delay value with delay, tail_prob (= 1 - G_D(delay)), lpd, relative_surprise.