flimoptim_Julia {flimo}

Internal flimoptim function in Julia mode

Description

Computes several parameter inferences with Julia optimizer and either IPNewton with or without Automatic Differentiation, Nelder-Mead or Brent method.

Usage

flimoptim_Julia(
  ndraw,
  data = NULL,
  dsumstats = NULL,
  simulatorQ = NULL,
  julia_obj = NULL,
  nsim = 10,
  ninfer = 1,
  lower = 0,
  upper = 1,
  Theta0 = (lower + upper)/2,
  randomTheta0 = FALSE,
  AD = TRUE,
  method = "",
  obj_threshold = Inf,
  number_tries = 1,
  maxit = 1000,
  time_limit = NULL,
  xtol = 0,
  ftol = 0,
  gtol = 1e-08,
  reltol = sqrt(.Machine$double.eps),
  abstol = .Machine$double.eps,
  show_trace = FALSE,
  store_trace = FALSE,
  store_quantiles = FALSE,
  par_names = NULL,
  load_julia = FALSE
)

Arguments

ndraw

Integer. Number of random variables to draw for one simulation of the model.

data

1D array containing the observations.

dsumstats

Summary statistics to measure distance between simulations and data. String containing the script of the Julia function dsumstats(simulations, data). The name "dsumstats" is mandatory.

simulatorQ

Simulator of the stochastic process with fixed quantiles (see README). simulatorQ(Theta, quantiles). The name "simulatorQ" is mandatory. Theta is the parameter set for the simulations and quantiles are drawn in U(0,1).

julia_obj

Objective function to minimize. Default : is directly computed from dsumstats and simulatorQ. String containing the script of the Julia function julia_obj(Theta, quantiles). Warning : can be tricky to call data. The name "julia_obj" is mandatory.

nsim

Integer. Number of simulations to run for each step of the optimization algorithm. Computation time grows linearly with this number. Default to 10.

ninfer

Integer. Number of independent inferences to run. Default to 1.

lower

1D array. Lower bounds for parameters. Same length as upper.

upper

1D array. Upper bounds for parameters. Same length as lower.

Theta0

1D array. Initial values of the parameters. Default : mean(lower, upper).

randomTheta0

Boolean. If True, Theta0 is randomly drawn between lower and upper bounds.

AD

Boolean. Only in Julia mod, uses Automatic Differentiation with IPNewton method. Default to true.

method

String. Allows to choose the optimization method : "Brent", "IPNewton" or "NelderMead". Default : IPNewton.

obj_threshold

Float. Threshold score. If Final value of objective is bigger, relaunch the inference if number_tries is not reached. The purpose is to avoid local minima. Default to Inf (no threshold).

number_tries

Integer. Number of tries (inferences) for the objective value to reach a point lower than obj_threshold. Default to 1.

maxit

Integer. Max number of iterations during optimization. Default to 1000.

time_limit

Float. Time limit in second for each inference. Default to no limit. Not available for Brent method.

xtol

Float. With IPNewton method : xtol option in Optim.Options. Default to 0.

ftol

Float. With IPNewton method : ftol option in Optim.Options. Default to 0.

gtol

Float. With IPNewton method : gtol option in Optim.Options. Default to 1e-8.

reltol

Float. With Brent method : reltol of Optim.optimize. Default is sqrt(.Machine$double.eps), about 1e-8.

abstol

Float. With Brent method : abstol of Optim.optimize. Default is .Machine$double.eps, about 1e-16.

show_trace

Boolean. If true, shows standard trace. Default to false.

store_trace

Boolean. If true, stores standard trace as an array of strings. Default to false. Not available for R mod.

store_quantiles

Boolean. If true, stores every quantiles used for inference, to reproduce the results. Default to false.

par_names

vector of names for parameters. Default is "par1", ..., "parn".

load_julia

Boolean. If true, run julia_load. It can take few seconds. Default to False.

Value

Object of class flimo_result (list) converted from Julia object containing every information about convergence results.