Create a dataframe of nowcast results from a single reporting triangle
Source:R/baselinenowcast.R
baselinenowcast.reporting_triangle.RdThis function ingests a single reporting_triangle object and generates a nowcast in the form of a baselinenowcast_df object.
This function implements a nowcasting workflow for a single reporting triangle:
allocate_reference_times()- Allocate the reference times used for delay and uncertainty estimationestimate_delay()- Estimate a reporting delay PMFapply_delay()- Generate a point nowcast using the delay PMFestimate_and_apply_uncertainty()- Generate a probabilistic nowcast from a point nowcast and reporting triangle
This function will by default estimate the delay from the
reporting_triangle and estimate uncertainty using past
retrospective nowcast errors on that reporting_triangle to
generate probabilistic nowcasts, which
are samples from the predictive distribution of the estimated final case
count at each reference date.
Alternatives include passing in a separate delay_pmf or
uncertainty_params.
This method specifically computes a nowcast for a single reporting
triangle. See documentation for the arguments of this function which
can be used to set the model specifications (things like number of
reference times for delay and uncertainty estimation, the observation
model, etc.).
Usage
# S3 method for class 'reporting_triangle'
baselinenowcast(
data,
scale_factor = 3,
prop_delay = 0.5,
output_type = c("samples", "point"),
draws = 1000,
uncertainty_model = fit_by_horizon,
uncertainty_sampler = sample_nb,
delay_pmf = NULL,
uncertainty_params = NULL,
preprocess = preprocess_negative_values,
validate = TRUE,
...
)Arguments
- data
reporting_triangle class object to be nowcasted. The matrix must contain missing observations in the form of NAs in order to generate an output from this function.
- scale_factor
Numeric value indicating the multiplicative factor on the maximum delay to be used for estimation of delay and uncertainty. Default is
3.- prop_delay
Numeric value <1 indicating what proportion of all reference times in the reporting triangle to be used for delay estimation. Default is
0.5.- output_type
Character string indicating whether the output should be samples (
"samples")from the estimate with full uncertainty or whether to return the point estimate ("point"). Default is"samples".- draws
Integer indicating the number of probabilistic draws to include if
output_typeis"samples". Default is 1000.- uncertainty_model
Function that ingests a matrix of observations and a matrix of predictions and returns a vector that can be used to apply uncertainty using the same error model. Default is
fit_by_horizonwith arguments ofobsmatrix of observations andpredthe matrix of predictions that fits each column (horizon) to a negative binomial observation model by default. The user can specify a different fitting model by replacing thefit_modelargument infit_by_horizon.- uncertainty_sampler
Function that ingests a vector or matrix of predictions and a vector of uncertainty parameters and generates draws from the observation model. Default is
sample_nbwhich expects argumentspredfor the vector of predictions and uncertainty parameters for the corresponding vector of uncertainty parameters, and draws from a negative binomial for each element of the vector.- delay_pmf
Vector of delays assumed to be indexed starting at the first delay column in the reporting triangle. Default is NULL, which will estimate the delay from the reporting triangle in
data. Seeestimate_delay()for more details.- uncertainty_params
Vector of uncertainty parameters ordered from horizon 1 to the maximum horizon. Default is
NULL, which will result in computing the uncertainty parameters from the reporting triangledata. Seeestimate_uncertainty()for more details.- preprocess
Function to apply to the truncated triangle before estimation, or NULL to skip preprocessing. Default is
preprocess_negative_values(), which handles negative values by redistributing them to earlier delays. Set to NULL if you want to preserve negative PMF entries (e.g., when working with downward corrections where negative probabilities reflect systematic adjustments). Custom preprocess functions must accept avalidateparameter (defaults to TRUE) to enable validation optimisation in internal function chains.- validate
Logical. If TRUE (default), validates the object. Set to FALSE only when called from functions that already validated.
- ...
Additional arguments passed to
estimate_uncertainty()andsample_nowcast().
Value
Data.frame of class baselinenowcast_df
See also
Main nowcasting interface functions
assert_baselinenowcast_df(),
baselinenowcast(),
baselinenowcast.data.frame(),
baselinenowcast_df-class,
new_baselinenowcast_df()
Examples
# Filter to recent data and truncate to reasonable max_delay for faster
# example
data_as_of_df <- syn_nssp_df[syn_nssp_df$report_date <= "2026-04-01", ]
rep_tri <- as_reporting_triangle(data = data_as_of_df) |>
truncate_to_delay(max_delay = 25) |>
tail(n = 40)
#> ℹ Using max_delay = 154 from data
#> ℹ Truncating from max_delay = 154 to 25.
nowcast_df <- baselinenowcast(rep_tri, draws = 100)
#> Warning: 40 reference times available and 75 are specified.
#> ℹ All 40 reference times will be used.
#> ℹ 0.5 reference times were specified for delay estimation but 0.825 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.
nowcast_df
#> pred_count reference_date draw output_type
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