Fit a nowcast with one modelling package

nowcast_fit() and nowcast_tidy() are the two extension points of the nowcasting framework. Together they teach run_nowcast() about a new modelling package: nowcast_fit() runs the model, nowcast_tidy() turns whatever it returned into the tidy quantile format every other function in tbl.now understands.

Dispatch happens on the object built by engine(), so a method for "mypackage" is a function called nowcast_fit.mypackage(). It can live in any package.

Usage

# S3 method for class 'diseasenowcasting'
nowcast_fit(
  engine,
  x,
  ...,
  quantile_levels = nowcast_quantile_levels(),
  verbose = TRUE
)

# S3 method for class 'baselinenowcast'
nowcast_fit(
  engine,
  x,
  ...,
  draws = 1000,
  delays_unit = NULL,
  max_delay = NULL,
  quantile_levels = nowcast_quantile_levels(),
  verbose = TRUE
)

# S3 method for class 'epinowcast'
nowcast_fit(
  engine,
  x,
  ...,
  preprocess_args = list(),
  quantile_levels = nowcast_quantile_levels(),
  verbose = TRUE
)

# S3 method for class 'NobBS'
nowcast_fit(
  engine,
  x,
  ...,
  specs = list(),
  quantile_levels = nowcast_quantile_levels(),
  verbose = TRUE
)

# S3 method for class 'surveillance'
nowcast_fit(
  engine,
  x,
  ...,
  when = NULL,
  D = NULL,
  fit_method = "bayes.notrunc.bnb",
  control = list(),
  quantile_levels = nowcast_quantile_levels(),
  verbose = TRUE
)

# S3 method for class 'EpiNow2'
nowcast_fit(
  engine,
  x,
  ...,
  convert_args = list(),
  quantile_levels = nowcast_quantile_levels(),
  verbose = TRUE
)

nowcast_fit(
  engine,
  x,
  ...,
  quantile_levels = nowcast_quantile_levels(),
  verbose = TRUE
)

# Default S3 method
nowcast_fit(
  engine,
  x,
  ...,
  quantile_levels = nowcast_quantile_levels(),
  verbose = TRUE
)

Arguments

engine

An engine() object – the modelling package plus its arguments. S3 dispatch is on its class, so a backend for "mypackage" is a function called nowcast_fit.mypackage(). The whole engine arrives, not just its name, so engine$args, engine$label and the rest are available to a backend that wants them.

x

A tbl_now object.

...

Arguments passed straight to the underlying modelling function. run_nowcast() splices the engine's own arguments in here.

quantile_levels

Numeric vector of probabilities. Most backends ignore it at fit time (the quantiles are computed from the draws afterwards), but some need to be told up front which levels to report.

verbose

Logical. Whether the backend (and the converters feeding it) should be chatty.

draws

("baselinenowcast" only) Number of nowcast samples to draw.

delays_unit

("baselinenowcast" only) Unit of the reporting triangle's delay axis; inferred from the object's time units when NULL.

max_delay

("baselinenowcast" only) Number of delay periods to keep, forwarded to tbl_now_to_baselinenowcast(). NULL keeps every delay, which a snapshot ("as of") series cannot be fitted with – see engine_baselinenowcast().

preprocess_args

("epinowcast" only) A list of arguments for tbl_now_to_epinowcast(), e.g. list(max_delay = 20).

specs

("NobBS" only) The specs list of NobBS::NobBS(). The quantiles element is filled from quantile_levels unless you set it.

when, D, fit_method, control

("surveillance" only) The when, D, method and control arguments of surveillance::nowcast(). when defaults to get_surveillance_when(x, length = D + 1), D to the largest delay in the data, and control$dRange to get_surveillance_range() – the grid running to get_now(), which a line list cannot express on its own. Both grids are built from the whole object, so every stratum is fitted on the same time axis. fit_method is surveillance's method argument, renamed so it cannot collide with run_nowcast()'s own method.

convert_args

("EpiNow2" only) A list of arguments for tbl_now_to_EpiNow2(), e.g. list(accumulate = FALSE).

Value

nowcast_fit() returns the modelling package's own object, verbatim. It is stored in the fit property of the resulting tbl_nowcast, and it is the only thing nowcast_tidy() is given besides the tbl_now itself, so put whatever the tidying step will need into it.

See also

nowcast_tidy(), run_nowcast(), list_nowcast_methods() and the Adding your own nowcasting model article for a worked example of a new backend.

Examples

# A minimal backend: two S3 methods and you are done.
nowcast_fit.constant <- function(engine, x, ..., quantile_levels, verbose = TRUE) {
  counts <- get_latest_reported_cases(x)
  list(dates = counts[[get_event_date(x)]], value = counts[[ncol(counts)]])
}

nowcast_tidy.constant <- function(engine, fit, x, ..., quantile_levels) {
  predictions <- tidyr::expand_grid(
    event_date = fit$dates, .quantile_level = quantile_levels
  )
  predictions$.value <- rep(fit$value, each = length(quantile_levels))
  names(predictions)[1] <- get_event_date(x)
  list(predictions = predictions, draws = NULL)
}