In this last case, the seed value should be used by model to initialize its pseudo-random number generators (if model is stochastic). If TRUE, ABC_sequential provides as input to the function model a vector containing an integer seed value and the model parameters used for the simulation. If FALSE (default), ABC_sequential provides as input to the function model a vector containing the model parameters used for the simulation. ![]() If larger than 1 (the default value), ABC_sequential will launch model simulations in parallel on n_cluster cores of the computer. If not provided, no constraint will be applied.Ī positive integer. in the same order as in the prior definition. Įach parameter should be designated with "X1", "X2". This expression will be evaluated as a logical expression, you can use all the logical operators including "". (2012) for details.Ī vector containing the targeted (observed) summary statistics.Ī string expressing the constraints between model parameters. See the package's vignette and Lenormand et al. When method is "Lenormand", the number of simulations below the tolerance threshold is equal to nb_simul * alpha. See the vignette for additional information on this topic.Ī positive integer equal to the desired number of simulations of the model below the tolerance threshold when method is "Beaumont", "Drovandi" and "Delmoral". User-defined prior distributions can also be provided. Note that when using the method "Lenormand", solely uniform prior distributions are supported. 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. 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. Each element of the list corresponds to a model parameter. 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).Ī list of prior information. Users may alternatively wish to wrap their binary executables using the provided functions binary_model and binary_model_cluster. When using the option use_seed=TRUE, model must take as arguments a vector containing a seed value and the model parameter values.Ī tutorial is provided in the package's vignette to dynamically link a binary code to a R function. It must take as arguments a vector of model parameter values and it must return a vector of summary statistics. Possible values are "Beaumont", "Drovandi", "Delmoral", "Lenormand" and "Emulation".Ī R function implementing the model to be simulated. Prior_test=NULL, n_cluster = 1, use_seed = FALSE, verbose = FALSE,Ī character string indicating the sequential algorithm to be used. Usage ABC_sequential(method, model, prior, nb_simul, summary_stat_target, Sequential sampling schemes have been shown to be more efficient than standard rejection-based procedures. Sequential sampling schemes consist in sampling initially model parameters in the prior distribution, just like in a standard rejection-based ABC, in order to obtain a rough posterior distribution of parameter values, and in subsequently sampling close to this rough posterior distribution to refine it. This function implements four different algorithms to perform sequential sampling schemes for ABC. Sequential sampling schemes for ABC Description
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