simdesign_ABCmcmc_Marjoram: Add an Approximate Bayesian Computation (Monte-Carlo...

In nlrx: Setup, Run and Analyze 'NetLogo' Model Simulations from 'R' via 'XML'

Add an Approximate Bayesian Computation (Monte-Carlo Markov-Chain) simdesign using the Majoram algorithm to a nl object

Description

Add an Approximate Bayesian Computation (Monte-Carlo Markov-Chain) simdesign using the Majoram algorithm to a nl object

Usage

simdesign_ABCmcmc_Marjoram(
  nl,
  postpro_function = NULL,
  summary_stat_target,
  prior_test = NULL,
  n_rec,
  n_between_sampling = 10,
  n_cluster = 1,
  use_seed = FALSE,
  dist_weights = NULL,
  n_calibration = 10000,
  tolerance_quantile = 0.01,
  proposal_phi = 1,
  seed_count = 0,
  progress_bar = FALSE,
  nseeds
)

Arguments

nl	nl object with a defined experiment
postpro_function	default is NULL. Allows to provide a function that is called to post-process the output Tibble of the NetLogo simulations. The function must accept the nl object with attached results as input argument. The function must return a one-dimensional vector of output metrics that corresponds in length and order to the specified summary_stat_target.
summary_stat_target	a vector of target values in the same order as the defined metrics of the experiment
prior_test	a string expressing the constraints between model parameters. This expression will be evaluated as a logical expression, you can use all the logical operators including "<", ">", ... Each parameter should be designated with "X1", "X2", ... in the same order as in the prior definition. Set to NULL to disable.
n_rec	Number of samples along the MCMC
n_between_sampling	a positive integer equal to the desired spacing between sampled points along the MCMC.
n_cluster	number of cores to parallelize simulations. Due to the design of the EasyABC parallelization it is currently not possible to use this feature with cores > 1.
use_seed	if TRUE, seeds will be automatically created for each new model run
dist_weights	a vector containing the weights to apply to the distance between the computed and the targeted statistics. These weights can be used to give more importance to a summary statistic for example. The weights will be normalized before applying them. Set to NULL to disable.
n_calibration	a positive integer. This is the number of simulations performed during the calibration step. Default value is 10000.
tolerance_quantile	a positive number between 0 and 1 (strictly). This is the percentage of simulations retained during the calibration step to determine the tolerance threshold to be used during the MCMC. Default value is 0.01.
proposal_phi	a positive number. This is a scaling factor defining the range of MCMC jumps. Default value is 1.
seed_count	a positive integer, the initial seed value provided to the function model (if use_seed=TRUE). This value is incremented by 1 at each call of the function model.
progress_bar	logical, FALSE by default. If TRUE, ABC_mcmc will output a bar of progression with the estimated remaining computing time. Option not available with multiple cores.
nseeds	number of seeds for this simulation design

Details

This function creates a simdesign S4 class which can be added to a nl object.

Variables in the experiment variable list need to provide a numeric distribution with min, max and a shape of the distribution (qunif, qnorm, qlnorm, qexp)(e.g. list(min=1, max=4, qfun="qunif")).

The function uses the EasyABC package to set up the ABC_mcmc function. For details on the ABC_mcmc function parameters see ?EasyABC::ABC_mcmc Finally, the function reports a simdesign object.

Approximate Bayesian Computation simdesigns can only be executed using the run_nl_dyn function instead of run_nl_all or run_nl_one.

Value

simdesign S4 class object

Examples

# To attach a simdesign, a nl object needs to be created first (see ?nl).
# For this example, we load a nl object from test data.

nl <- nl_lhs

# Attach the simdesign to the nl object
nl@simdesign <- simdesign_ABCmcmc_Marjoram(nl = nl,
                                            summary_stat_target = c(100, 80),
                                            n_rec = 100,
                                            n_between_sampling = 10,
                                            n_cluster = 1,
                                            use_seed = FALSE,
                                            n_calibration = 10000,
                                            tolerance_quantile = 0.01,
                                            proposal_phi = 1,
                                            progress_bar = FALSE,
                                            nseeds = 1)

nlrx documentation built on May 31, 2023, 8:34 p.m.