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 will by
default estimate uncertainty using past retrospective nowcast errors and
generate probabilistic nowcasts, which are samples from the predictive
distribution of the estimated final case count at each reference date.
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,
...
)Arguments
- data
reporting_triangleclass object to be nowcasted.- 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
data$reporting_triangle_matrix. Default is NULL, which will estimate the delay from the reporting triangle matrix indata, Seeestimate_delayfor more details.- uncertainty_params
Vector of uncertainty parameters ordered from horizon 1 to the maximum horizon. Default is
NULL, which will result in computing the uncertainty parameters from the reporting triangle matrixdata. Seeestimate_uncertaintyfor more details.- ...
Additional arguments passed to
estimate_uncertaintyandsample_nowcast.
Value
Data.frame of class baselinenowcast_df
See also
Other baselinenowcast_df:
baselinenowcast(),
baselinenowcast_df-class
Examples
data_as_of_df <- syn_nssp_df[syn_nssp_df$report_date <= "2026-04-01", ]
rep_tri <- as_reporting_triangle(
data = data_as_of_df,
max_delay = 25
)
nowcast_df <- baselinenowcast(rep_tri)
#> ℹ 0.5 reference times were specified for delay estimation but 0.493 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 draw reference_date output_type
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