ABC_mcmc | R Documentation |
This function implements three different algorithms to perform coupled to MCMC ABC.
ABC_mcmc(method, model, prior, summary_stat_target, prior_test=NULL, n_rec=100, n_between_sampling=10, n_cluster = 1, use_seed = FALSE, verbose = FALSE, dist_weights=NULL, ...)
method |
a character string indicating the ABC-MCMC algorithm to be used. Possible values are |
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 |
summary_stat_target |
a vector containing the targeted (observed) summary statistics. |
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 |
n_rec |
a positive integer equal to the desired number of sampled points along the MCMC. |
n_between_sampling |
a positive integer equal to the desired spacing between sampled points along the MCMC. |
n_cluster |
a positive integer. If larger than 1 (the default value), |
use_seed |
logical. If |
verbose |
logical. |
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. If not provided, no weights will be applied. |
... |
Additional arguments can be passed depending on the choosen method (see below) |
See the package's vignette for details on ABC-MCMC.
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 |
dist |
The distance of the simulations to the data. |
stats_normalization |
The standard deviation of the summary statistics across the |
epsilon |
The final maximal distance between simulations and data in the retained sample of particles. |
nsim |
The number of |
n_between_sampling |
The spacing between two sampled points in the MCMC. |
computime |
The computing time to perform the simulations. |
min_stats |
The minimal values of each summary statistics during the calibration step, given when |
max_stats |
The maximal values of each summary statistics during the calibration step, given when |
lambda |
The lambda values of the Box-Cox transformation, given when |
geometric_mean |
The geometric means, given when |
boxcox_mean |
The means of Box-Cox transforms, given when |
boxcox_sd |
The standard deviations of Box-Cox transforms, given when |
pls_transform |
The matrix of PLS transformation, given when |
numcomp |
The number of used components for the PLS transformation, given when |
Depending on the choosen method, you can specify the following arguments:
a positive number, used when method
is "Marjoram_original"
.
This is the tolerance threshold used during the MCMC.
If not provided by the user, it is automatically computed as half the distance between the first simulation and the target summary statistics and a warning is printed.
a vector of the same length as summary_stat_target
, used when method
is "Marjoram_original"
.
Each element contains a positive number by which each summary statistics must be divided before the computation of the Euclidean distance between simulations and data.
If not provided by the user, the simulated summary statistics are divided by the target summary statistics and a warning is printed.
a vector of the same length as the number of model parameters, used when method
is "Marjoram_original"
.
Each element contains a positive number defining the range of MCMC jumps for each model parameter.
If not provided by the user, a default value is used for each parameter and a warning is printed. The default value is 1/50 of the prior range for uniform distributions, 1/20 of the standard deviation of the prior distribution for normal distributions, 1/20 * exp ( sigma * sigma
for lognormal distributions where sigma is the standard deviation of the prior distribution in the log scale, and 1/20 of the inverse of the rate for exponential distributions.
a positive integer, used when method
is "Marjoram"
or "Wegmann"
.
This is the number of simulations performed during the calibration step.
Default value is 10000.
a positive number between 0 and 1 (strictly), used when method
is "Marjoram"
or "Wegmann"
.
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.
a positive number, used when method
is "Marjoram"
or "Wegmann"
.
This is a scaling factor defining the range of MCMC jumps.
Default value is 1.
a positive integer, used when method
is "Wegmann"
.
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.
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
.
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.
a positive number, the max number of fails when moving particle inside the prior. Enabled only if inside_prior is to TRUE
.
10000
by default.
Franck Jabot, Thierry Faure and Nicolas Dumoulin
Marjoram, P., Molitor, J., Plagnol, V. and Tavar\'e, S. (2003) Markov chain Monte Carlo without likelihoods. PNAS, 100, 15324–15328.
Wegmann, D., Leuenberger, C. and Excoffier, L. (2009) Efficient approximate Bayesian computation coupled with Markov chain Monte Carlo without likelihood. Genetics, 182, 1207-1218.
binary_model
, binary_model_cluster
, ABC_rejection
, ABC_emulation
, ABC_sequential
## Not run: ##### EXAMPLE 1 ##### ##################### ## the model has two parameters and outputs two summary statistics. ## defining a simple toy model: toy_model<-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_prior=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. ## define the targeted summary statistics sum_stat_obs=c(1.5,0.5) ## to perform the Marjoram et al. (2003)'s method: ## ABC_Marjoram_original<-ABC_mcmc(method="Marjoram_original", model=toy_model, prior=toy_prior, summary_stat_target=sum_stat_obs) ABC_Marjoram_original ## artificial example to perform the Marjoram et al. (2003)'s method, with modifications # drawn from Wegmann et al. (2009) without Box-Cox and PLS transformations. ## ABC_Marjoram<-ABC_mcmc(method="Marjoram", model=toy_model, prior=toy_prior, summary_stat_target=sum_stat_obs) ABC_Marjoram ## artificial example to perform the Wegmann et al. (2009)'s method. ## ABC_Wegmann<-ABC_mcmc(method="Wegmann", model=toy_model, prior=toy_prior, summary_stat_target=sum_stat_obs) ABC_Wegmann ##### EXAMPLE 2 ##### ##################### ## this time, the model is a C++ function packed into a R function -- this time, # the option 'use_seed' must be turned to TRUE. ## define prior information 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 ## define the targeted summary statistics sum_stat_obs=c(100,2.5,20,30000) ## artificial example to perform the Marjoram et al. (2003)'s method. ## n=10 ABC_Marjoram_original<-ABC_mcmc(method="Marjoram_original", model=trait_model, prior=trait_prior, summary_stat_target=sum_stat_obs, n_rec=n, use_seed=TRUE) ABC_Marjoram_original ## artificial example to perform the Marjoram et al. (2003)'s method, with modifications # drawn from Wegmann et al. (2009) without Box-Cox and PLS transformations. ## n=10 n_calib=10 tol_quant=0.2 ABC_Marjoram<-ABC_mcmc(method="Marjoram", model=trait_model, prior=trait_prior, summary_stat_target=sum_stat_obs, n_rec=n, n_calibration=n_calib, tolerance_quantile=tol_quant, use_seed=TRUE) ABC_Marjoram ## artificial example to perform the Wegmann et al. (2009)'s method. ## n=10 n_calib=10 tol_quant=0.2 ABC_Wegmann<-ABC_mcmc(method="Wegmann", model=trait_model, prior=trait_prior, summary_stat_target=sum_stat_obs, n_rec=n, n_calibration=n_calib, tolerance_quantile=tol_quant, use_seed=TRUE) ABC_Wegmann ## End(Not run)
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