R/simulation_one_env.R

In YoannAnciaux/dfeSingleMutationsAcrossEnvironmentsBankAndBolon: Compendium of the results for the inference of FGM parameters from 115 single mutations.

# library(inferenceFitnessLandscape)
# library(here)
# library(abc)
#
# #### Parameters for simulations ####
# rgeom_trunc <- function(n, a, b, prob) {truncdist::rtrunc(n, spec = "geom", a, b, prob)}
# rexp_trunc <- function(n, a, b, rate) {truncdist::rtrunc(n, spec = "exp", a, b, rate)}
# fitness_wt <- read.table(file = system.file("raw_data", "fake_115_mutations_environment_reference.csv", package = "dfeSingleMutationsAcrossEnvironmentsBankAndBolon"), skip = 1)[1, 116]
# nb_simul <- 10^6
# pn <- 1/5
# rlambda <- 1/0.2
# rmaxfitness <- 1/2
# alpha_inf <- 0.1; alpha_sup <- 10
# Q_inf <- 0.5; Q_sup <- 6
# n_prior <- rgeom_trunc(nb_simul, 0, Inf, pn)
# matrix_param <- cbind(n = n_prior,
#                       lambda = rexp(nb_simul, rlambda),
#                       maxfitness = rexp_trunc(nb_simul, fitness_wt, Inf, rmaxfitness),
#                       alpha = runif(nb_simul, alpha_inf, alpha_sup),
#                       Q = runif(nb_simul, Q_inf, Q_sup),
#                       m = n_prior)
# save("./simulation/115_mutations_fgmrmut_parameters.Rda")
#
#
# #### Simulations ####
# sim <- simulate_fl(parameter = matrix_param, simulation_model = "fgmrmut",
#                    empirical_fl = here("inst", "raw_data", "fake_115_mutations_environment_reference.csv"), ncore = 7, fun_args = fun_args,
#                    file_output = here("inst", "simulation", "115_mutations_fgmrmut_simulations.csv"),
#                    multi_file = F, output_args = list(sep = "\t"), skip = 1)
#
# #### Estimation ####
# input <- read_clean_output(file = here("inst", "simulation", "115_mutations_fgmrmut_simulations.csv"),
#                            keep_param = c("n", "lambda", "maxfitness", "alpha", "Q"),
#                            header = TRUE, sep = "\t")
#
#
#
# target <- matrix(read.table(file = here("inst", "raw_data", "fake_115_mutations_environment_reference.csv"), skip = 1)[2:116, 116], nrow = 1)
#
# abc_out <- list()
# abc_out$rejection <- abc(target = target, param = input$parameter,
#                          sumstat = input$simulation, tol = 0.01,
#                          method ="rejection")
# abc_out$loclinear <- abc(target = target, param = input$parameter,
#                          sumstat = input$simulation, tol = 0.01,
#                          method = "loclinear")
# abc_out$neuralnet <- abc(target = target, param = input$parameter,
#                          sumstat = input$simulation, tol = 0.01,
#                          method = "neuralnet")
# abc_out$ridge <- abc(target = target, param = input$parameter,
#                      sumstat = input$simulation, tol = 0.01,
#                      method = "ridge")
#
# summary(abc_out$rejection)
# summary(abc_out$loclinear)
# summary(abc_out$neuralnet)
# summary(abc_out$ridge)
#
#
# #### CV ####
#
# cv_out <- list()
# cv_out$rejection <- cv4abc(param = input$parameter,
#        sumstat = input$simulation, tols = 0.01,
#        nval = 10, abc.out = abc_out$rejection)