##### EXAMPLE 1 #####
#####################
set.seed(1)
## artificial example to show how to use the 'ABC_rejection' function.
## defining a simple toy model:
toy_model<-function(x){ 2 * x + 5 + rnorm(1,0,0.1) }
## define prior information
toy_prior=list(c("unif",0,1)) # a uniform prior distribution between 0 and 1
## only launching simulations with parameters drawn in the prior distributions
set.seed(1)
n=10
ABC_sim<-ABC_rejection(model=toy_model, prior=toy_prior, nb_simul=n)
ABC_sim
## launching simulations with parameters drawn in the prior distributions
# and performing the rejection step
sum_stat_obs=6.5
tolerance=0.2
ABC_rej<-ABC_rejection(model=toy_model, prior=toy_prior, nb_simul=n,
summary_stat_target=sum_stat_obs, tol=tolerance)
## NB: see the package's vignette to see how to pipeline 'ABC_rejection' with the function
# 'abc' of the package 'abc' to perform other rejection schemes.
if (FALSE) {
##### EXAMPLE 2 #####
#####################
## this time, the model has two parameters and outputs two summary statistics.
## defining a simple toy model:
toy_model2<-function(x){ c( x[1] + x[2] + rnorm(1,0,0.1) , x[1] * x[2] + rnorm(1,0,0.1) ) }
## define prior information
toy_prior2=list(c("unif",0,1),c("normal",1,2))
# a uniform prior distribution between 0 and 1 for parameter 1, and a normal distribution
# of mean 1 and standard deviation of 2 for parameter 2.
## only launching simulations with parameters drawn in the prior distributions
set.seed(1)
n=10
ABC_sim<-ABC_rejection(model=toy_model2, prior=toy_prior2, nb_simul=n)
ABC_sim
## launching simulations with parameters drawn in the prior distributions
# and performing the rejection step
sum_stat_obs2=c(1.5,0.5)
tolerance=0.2
ABC_rej<-ABC_rejection(model=toy_model2, prior=toy_prior2, nb_simul=n,
summary_stat_target=sum_stat_obs2, tol=tolerance)
## NB: see the package's vignette to see how to pipeline 'ABC_rejection' with the function
# 'abc' of the package 'abc' to perform other rejection schemes.
##### EXAMPLE 3 #####
#####################
## this time, the model is a C++ function packed into a R function -- this time, the option
# 'use_seed' must be turned to TRUE.
n=10
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_sim<-ABC_rejection(model=trait_model, prior=trait_prior, nb_simul=n, use_seed=TRUE)
ABC_sim
## launching simulations with parameters drawn in the prior distributions and performing
# the rejection step
sum_stat_obs=c(100,2.5,20,30000)
tolerance=0.2
ABC_rej<-ABC_rejection(model=trait_model, prior=trait_prior, nb_simul=n,
summary_stat_target=sum_stat_obs, tol=tolerance, use_seed=TRUE)
## NB: see the package's vignette to see how to pipeline 'ABC_rejection' with the function
# 'abc' of the package 'abc' to perform other rejection schemes.
##### EXAMPLE 4 - Parallel implementations #####
################################################
## NB: the option use_seed must be turned to TRUE.
## For models already running with the option use_seed=TRUE, simply change
# the value of n_cluster:
sum_stat_obs=c(100,2.5,20,30000)
ABC_simb<-ABC_rejection(model=trait_model, prior=trait_prior, nb_simul=n,
use_seed=TRUE, n_cluster=2)
## For other models, change the value of n_cluster and modify the model so that the first
# parameter becomes a seed information value:
toy_model_parallel<-function(x){
set.seed(x[1])
2 * x[2] + 5 + rnorm(1,0,0.1) }
sum_stat_obs=6.5
ABC_simb<-ABC_rejection(model=toy_model_parallel, prior=toy_prior, nb_simul=n,
use_seed=TRUE, n_cluster=2)
}
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