- model
a R function implementing the model to be simulated. It must take as arguments a vector of model parameter values and it must return a vector of summary statistics. When using the option use_seed=TRUE, model must take as arguments a vector containing a seed value and the model parameter values.
A tutorial is provided in the package's vignette to dynamically link a binary code to a R function. Users may alternatively wish to wrap their binary executables using the provided functions binary_model and binary_model_cluster. The use of these functions is associated with slightly different constraints on the design of the binary code (see binary_model and binary_model_cluster).
- prior
a list of prior information. Each element of the list corresponds to a model parameter. The list element must be a vector whose first argument determines the type of prior distribution: possible values are "unif" for a uniform distribution on a segment, "normal" for a normal distribution, "lognormal" for a lognormal distribution or "exponential" for an exponential distribution.
The following arguments of the list elements contain the characteritiscs of the prior distribution chosen: for "unif", two numbers must be given: the minimum and maximum values of the uniform distribution; for "normal", two numbers must be given: the mean and standard deviation of the normal distribution; for "lognormal", two numbers must be given: the mean and standard deviation on the log scale of the lognormal distribution; for "exponential", one number must be given: the rate of the exponential distribution. User-defined prior distributions can also be provided. See the vignette for additional information on this topic.
- nb_design_pts
a positive integer equal to the desired number of simulations of the model used to build the emulator.
- nb_simul
a positive integer equal to the desired number of simulations of the emulator.
- 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.
If not provided, no constraint will be applied.
- summary_stat_target
a vector containing the targeted (observed) summary statistics.
If not provided, ABC_rejection only launches the simulations and outputs the simulation results.
- emulator_span
a positive number, the number of design points selected for the local regression.
50 by default.
- tol
tolerance, a strictly positive number (between 0 and 1) indicating the proportion of simulations retained nearest the targeted summary statistics.
- use_seed
logical. If FALSE (default), ABC_rejection provides as input to the function model a vector containing the model parameters used for the simulation.
If TRUE, ABC_rejection provides as input to the function model a vector containing an integer seed value and the model parameters used for the simulation.
In this last case, the seed value should be used by model to initialize its pseudo-random number generators (if model is stochastic).
- 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.
- n_cluster
a positive integer. If larger than 1 (the default value), ABC_rejection will launch model simulations in parallel on n_cluster cores of the computer.
- verbose
logical. FALSE by default. If TRUE, ABC_rejection writes in the current directory intermediary results at the end of each step of the algorithm in the file "output".
These outputs have a matrix format, in wich each raw is a different simulation, the first columns are the parameters used for this simulation, and the last columns are the summary statistics of this simulation.
- progress_bar
logical, FALSE by default. If TRUE, ABC_rejection will output a bar of progression with the estimated remaining computing time. Option not available with multiple cores.