Optimise the capacity profile for projections

Usage

optimise_capacity(
  t_1_capacity,
  referrals_projections,
  incomplete_pathways,
  renege_capacity_params,
  target,
  target_bin,
  tolerance,
  max_iterations = 50
)

Arguments

t_1_capacity: numeric; a single value for the capacity for the first time period
referrals_projections: numeric; a vector for the number of referrals for each period in the projected time period
incomplete_pathways: tibble; two column data frame or tibble, with fields called months_waited_id (taking values 0 to the maximum months waited group of interest), and incompletes (the count of the number of incomplete pathways) representing the count of incomplete pathways at timestep 0 (to initialise the model with)
renege_capacity_params: tibble; three column data frame or tibble, with fields called months_waited_id (taking values 0 to the maximum months waited group of interest), and fields called capacity_param and renege_param, which are outputs from the function calibrate_capacity_renege_params()
target: string length 1; can be either a percentage point change, eg, "~-5%" or a percent value, eg, "5%"
target_bin: numeric length 1; the bin that the target refers to. It must be less than or equal to the max_months_waited value
tolerance: numeric length 1; the tolerance used to compare the absolute error with in the max_months_waited bin to determine convergence. The absolute error is calculated on the proportion in the max_months_waited bin relative to the total waiting (even if a non-percentage target is used)
max_iterations: numeric; the maximum number of iterations to test for convergence before providing a warning and an invalid number

Value

a capacity multiplier representing the annual change in capacity (from the input t_1_capacity to a capacity at t = 13) to achieve the desired target within the target tolerance. The name of the returned object provides an indication of whether the optimiser converged