Calibrates the reproductive rate and dispersal scales of the pops model.

Either Approximate Bayesian Computation or Markov Chain Monte Carlo Approximation is used to estimate relevant model parameters. Model accuracy is gauged using a custom quantity allocation disagreement function to assess accuracy of spatial configuration. We test number of predictions, number of predicted locations, cumulative distance to nearest infection. The calibration uses these metrics to determine if a run is kept if it is under a threshold. either because it improves the results or randomly gets kept despite being worse. We recommend running calibration for at least 10,000 iterations but even more will provide a better result. If the model converges and doesn't improve for awhile it will exist calibration prior to reaching the total number of iterations specified.

Usage

calibrate(config_rds_file)

Arguments

config_rds_file

Path to config file produced when calling `configuration`. The config file includes all data necessary used to set up c++ PoPS model

Value

a dataframe of the variables saved and their success metrics for each run