This function ingests data to be nowcasted and generates a
a baselinenowcast_df which contains a probabilistic or point
estimate of the final case counts at each reference date in the data.
See baselinenowcast.reporting_triangle() for details on the
input requirements.
Usage
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,
...
)Arguments
- data
Data 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". If"point"estimates are specified, the minimum number of reference times needed is the number needed for delay estimation, otherwise, if"samples"are specified, at least 2 additional reference times are required for uncertainty estimation.- 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.- ...
Additional arguments passed to methods.
Value
Data.frame of class baselinenowcast_df
Examples
# Generate a point nowcast from a reporting triangle
nowcast <- baselinenowcast(
example_reporting_triangle,
output_type = "point"
)
#> Warning: 7 reference times available and 9 are specified.
#> ℹ All 7 reference times will be used.
#> ℹ 0.5 reference times were specified for delay estimation but 0.857 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
#> reference_date pred_count draw output_type nowcast
#> 2024-01-01 2024-01-01 197.0000 1 point FALSE
#> 2024-01-02 2024-01-02 142.0000 1 point FALSE
#> 2024-01-03 2024-01-03 165.0000 1 point FALSE
#> 2024-01-04 2024-01-04 180.5919 1 point TRUE
#> 2024-01-05 2024-01-05 168.8218 1 point TRUE
#> 2024-01-06 2024-01-06 212.7238 1 point TRUE
#> 2024-01-07 2024-01-07 183.7663 1 point TRUE
# Generate probabilistic nowcast with samples
baselinenowcast(
example_reporting_triangle,
output_type = "samples",
draws = 100
)
#> Warning: 7 reference times available and 9 are specified.
#> ℹ All 7 reference times will be used.
#> ℹ 0.5 reference times were specified for delay estimation but 0.714 of reference times used for delay estimation.
#> ℹ This is due to the minumim requirement for the number of retrospective nowcasts for uncertainty estimation (2).
#> pred_count reference_date draw output_type nowcast
#> 1 197 2024-01-01 1 samples FALSE
#> 8 197 2024-01-01 2 samples FALSE
#> 15 197 2024-01-01 3 samples FALSE
#> 22 197 2024-01-01 4 samples FALSE
#> 29 197 2024-01-01 5 samples FALSE
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