baselinenowcast (version 0.2.0)

baselinenowcast: Generate a nowcast

Description

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,
  ...
)

Value

Data.frame of class baselinenowcast_df

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_type is "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_horizon with arguments of obs matrix of observations and pred the 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 the fit_model argument in fit_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_nb which expects arguments pred for 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.

See Also

Main nowcasting interface functions assert_baselinenowcast_df(), baselinenowcast.data.frame(), baselinenowcast.reporting_triangle(), baselinenowcast_df-class, new_baselinenowcast_df()

Examples

# Generate a point nowcast from a reporting triangle
nowcast <- baselinenowcast(
  example_reporting_triangle,
  output_type = "point"
)
nowcast

# Generate probabilistic nowcast with samples
baselinenowcast(
  example_reporting_triangle,
  output_type = "samples",
  draws = 100
)