Tidy a fitted nowcast into one standard table

Every nowcasting package returns its answer in its own shape – a matrix of posterior draws, an stsNC object, a Stan fit, an INLA summary, a bare list. tidy() turns any of them into the same table, so downstream code (plotting, scoring, comparison) does not care which engine produced it.

Usage

tidy(x, ...)

# S3 method for class 'baselinenowcast_df'
tidy(x, probs = NULL, ...)

# S3 method for class 'epinowcast'
tidy(x, probs = NULL, ...)

# S3 method for class 'stsNC'
tidy(x, probs = NULL, ...)

# S3 method for class 'estimate_infections'
tidy(x, probs = NULL, ...)

# S3 method for class 'epinow'
tidy(x, probs = NULL, ...)

# S3 method for class 'estimate_truncation'
tidy(x, probs = NULL, ...)

# S3 method for class 'list'
tidy(x, probs = NULL, engine = NULL, level = NULL, ...)

Arguments

x: A fitted nowcast. See Supported objects.
...: Passed to methods.
probs: Optional numeric vector of probabilities in [0, 1]. Adds a q* column per probability. Only available for engines that expose draws.
engine: Optional string naming the engine. Needed only for the shapes that arrive as an unclassed list – a NobBS fit, an EpiNow2::regional_epinow() result, or a per-stratum list of baselinenowcast or surveillance::nowcast() fits – which are otherwise recognised by their structure.
level: Interval width to report for an engine that does not say what it produced. Only used by the NobBS branch (see level under Value); NULL, the default, reports NA.

Value

A tibble, as described above.

Value

A tibble with one row per event date (per stratum, where the fit carries strata) and these columns:

event_date: Date. The event/reference date, on the engine's own grid. tidy() deliberately does not re-grid: some packages bin onto week starts of their own choosing, and silently snapping them would hide a real difference. Align afterwards if you need to.
stratum: character. One label per stratum the fit reports, and "all" when the fit is unstratified. Several stratifying columns are pasted " | "-separated, matching the triangle_list naming of tbl_now_to_baselinenowcast(). (stratum, event_date) is therefore a unique key.
estimate: numeric. The point nowcast – the posterior median where the engine provides draws or a median, otherwise its point estimate.
conf.low, conf.high: numeric. Interval bounds, following broom's naming. NA when the engine returns no interval.
level: numeric. The width the interval actually has, e.g. 0.95. Engines differ – epinowcast reports a 90% band by default while others report 95% – and without this column those get compared as if they were the same thing. NA whenever the width cannot be established: because the engine returned no interval (a baselinenowcast fit made with output_type = "point"), or because it returned one without saying how wide it is. NobBS is the latter case – its lower/upper come from specs$conf, and NobBS() does not return specs – so pass level yourself if you need it filled in. A guessed default is worse than NA in the one column that exists to stop widths being compared blindly.
engine: character. Which package produced the fit.

When probs is supplied, one extra column per requested quantile is appended, named q5, q50, q95 and so on (the probability times 100, so 0.025 becomes q2.5).

Which engines can honour `probs`

Only the engines that expose draws can compute an arbitrary quantile: diseasenowcasting, baselinenowcast and epinowcast. The others report a fixed set of summaries and nothing else, so asking them for a quantile they did not compute is an error rather than a silent approximation.

Supported objects

nowcast_prediction (S7) from diseasenowcasting::predict()
baselinenowcast_df from baselinenowcast::baselinenowcast()
epinowcast fits
stsNC from surveillance::nowcast()
the list returned by NobBS::NobBS() or by NobBS::NobBS.strat() (the stratified variant is recognised by its stratum column)
a list of baselinenowcast_df fits, one per stratum – what lapply()-ing over a tbl_now_triangle_list produces. Each element is tidied and labelled with its list name, giving the same one-block-per-stratum table the natively stratified engines return.

Examples

data(denguedat)
# A few years of data and a small number of draws, to keep the example quick.
dengue <- tbl_now(denguedat[1:10000, ],
  event_date = "onset_week", report_date = "report_week", verbose = FALSE
)
triangle <- suppressWarnings(
  tbl_now_to_baselinenowcast(dengue, verbose = FALSE)
)
#> ℹ Using max_delay = 15 from data
fit <- baselinenowcast::baselinenowcast(
  triangle, output_type = "samples", draws = 25
)
#> ℹ 0.5 reference times were specified for delay estimation but 0.489 of reference times used for delay estimation.
#> ℹ `prop_delay` not identical to the proportion of reference times used for delay estimation due to rounding.
tidy(fit)
#> # A tibble: 191 × 7
#>    event_date stratum estimate conf.low conf.high level engine         
#>    <date>     <chr>      <dbl>    <dbl>     <dbl> <dbl> <chr>          
#>  1 1990-01-01 all           61       61        61  0.95 baselinenowcast
#>  2 1990-01-08 all           50       50        50  0.95 baselinenowcast
#>  3 1990-01-15 all           44       44        44  0.95 baselinenowcast
#>  4 1990-01-22 all           46       46        46  0.95 baselinenowcast
#>  5 1990-01-29 all           39       39        39  0.95 baselinenowcast
#>  6 1990-02-05 all           34       34        34  0.95 baselinenowcast
#>  7 1990-02-12 all           24       24        24  0.95 baselinenowcast
#>  8 1990-02-19 all           17       17        17  0.95 baselinenowcast
#>  9 1990-02-26 all           17       17        17  0.95 baselinenowcast
#> 10 1990-03-05 all           16       16        16  0.95 baselinenowcast
#> # ℹ 181 more rows

Tidy a fitted nowcast into one standard table

Usage

Arguments

Value

Value

Which engines can honour probs

Supported objects

See also

Examples

Which engines can honour `probs`