Description Usage Arguments Value Author(s) References See Also Examples
This function launches a series of nb_design_pts
model simulations with model parameters drawn in the prior distribution specified in prior_matrix
, build an emulator with these computed design points and then launches a series of nb_simul
emulator simulations.
1 2 3 |
model |
a |
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 |
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 |
summary_stat_target |
a vector containing the targeted (observed) summary statistics.
If not provided, |
emulator_span |
a positive number, the number of design points selected for the local regression.
|
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 |
seed_count |
a positive integer, the initial seed value provided to the function |
n_cluster |
a positive integer. If larger than 1 (the default value), |
verbose |
logical. |
progress_bar |
logical, |
The returned value is a list containing the following components:
param |
The model parameters used in the |
stats |
The summary statistics obtained at the end of the |
weights |
The weights of the different |
stats_normalization |
The standard deviation of the summary statistics across the |
nsim |
The number of |
nrec |
The number of retained simulations (if targeted summary statistics are provided). |
computime |
The computing time to perform the simulations. |
Franck Jabot, Thierry Faure and Nicolas Dumoulin
Jabot, F., Lagarrigues G., Courbaud B., Dumoulin N. (2015). A comparison of emulation methods for Approximate Bayesian Computation. To be published.
binary_model
, binary_model_cluster
, ABC_sequential
, ABC_mcmc
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 | ## Not run:
##### EXAMPLE 1 #####
#####################
## the model is a C++ function packed into a R function -- the option 'use_seed'
## must be turned to TRUE.
trait_prior=list(c("unif",3,5),c("unif",-2.3,1.6),c("unif",-25,125),c("unif",-0.7,3.2))
trait_prior
## only launching simulations with parameters drawn in the prior distributions
ABC_emul = ABC_emulation(model=trait_model, prior=trait_prior,
nb_design_pts=10, nb_simul=300, use_seed=TRUE, progress=TRUE)
ABC_emul
## launching simulations with parameters drawn in the prior distributions and performing
# the rejection step
sum_stat_obs=c(100,2.5,20,30000)
ABC_emul = ABC_emulation(model=trait_model, prior=trait_prior, tol=0.2, nb_design_pts=10,
nb_simul=100, summary_stat_target=sum_stat_obs, use_seed=TRUE, progress=TRUE)
ABC_emul
## End(Not run)
|
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