Add an Approximate Bayesian Computation (Monte-Carlo Markov-Chain) simdesign using the Wegmann algorithm to a nl object
Add an Approximate Bayesian Computation (Monte-Carlo Markov-Chain) simdesign using the Wegmann algorithm to a nl object
simdesign_ABCmcmc_Wegmann( 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, numcomp = 0, seed_count = 0, progress_bar = FALSE, nseeds )
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 leght 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 statistisc 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. |
numcomp |
a positive integer. This is the number of components to be used for PLS transformations. Default value is 0 which encodes that this number is equal to the number of summary statistics. |
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 |
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.
simdesign S4 class object
# 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_Wegmann(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)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.