flimoptim: Main function to use flimo inference

In flimo: Fixed Landscape Inference Method

Main function to use flimo inference

Description

Computes several parameter inferences with R optimizer or Julia optimizer in a full Julia mode. In R mode (default) : L-BFGS-B optimization or other methods available for the base::optim function. In Julia mode : either IPNewton with or without Automatic Differentiation, Nelder-Mead or Brent optimization. Argument ndraw is mandatory. You need either to provide data, dsumstats AND simulatorQ OR obj.

Usage

flimoptim(
  ndraw,
  data = NULL,
  dsumstats = NULL,
  simulatorQ = NULL,
  obj = NULL,
  nsim = 10,
  ninfer = 1,
  lower = 0,
  upper = 1,
  Theta0 = (lower + upper)/2,
  randomTheta0 = FALSE,
  mode = c("R", "Julia"),
  AD = TRUE,
  method = "",
  obj_threshold = Inf,
  number_tries = 1,
  maxit = 1000,
  time_limit = NaN,
  factr = 1e+07,
  pgtol = 0,
  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. In R mode : R function of type dsumstats(simulations, data) where simulations : 2D array and data : 1D array. ncol(simulations) = length(data) mandatory. In Julia mode : a 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). or a string (in mode "Julia") containing the script of the Julia function simulatorQ(Theta, quantiles). In Julia mode, the name "simulatorQ" is mandatory. Theta is the parameter set for the simulations and quantiles are drawn in U(0,1).
obj	Objective function to minimize. Default : is directly computed from dsumstats and simulatorQ. Either an R function of type objective(Theta, quantiles) (in mode "R") or a string (in mode "Julia") containing the script of the Julia function julia_obj(Theta, quantiles). Warning : could be tricky if mode = "Julia" to call data. In Julia mode, 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. With Nelder-Mead in Julia mode: only used for starting point.
upper	1D array. Upper bounds for parameters. Same length as lower. With Nelder-Mead in Julia mode: only used for starting point.
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.
mode	String. "R" (default) or "Julia". See README.
AD	Boolean. Only in Julia mode, uses Automatic Differentiation with IPNewton method. Default to true.
method	String. In Julia mode, allows to choose the optimization method : "IPNewton", "Brent" or "NelderMead". Default : IPNewton. In R mode, allows to choose any of the optimization methods used by base::optim. Default is L-BFGS-B. Random methods do not work with flimo. Bounded methods are L-BFGS-B and Brent.
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 R mode and Brent method in Julia mode.
factr	Float. In R-mode : control parameter for L-BFGS-B method in stats::optim. Default to 1e7.
pgtol	Float. In R-mode : control parameter for L-BFGS-B method in stats::optim. Default to 0.
xtol	Float. In Julia mode with IPNewton method : xtol option in Optim.Options. Default to 0.
ftol	Float. In Julia mode with IPNewton method : ftol option in Optim.Options.
gtol	Float. In Julia mode with IPNewton method : gtol option in Optim.Options. Default to 1e-8.
reltol	Float. In Julia mode with Brent method : reltol of Optim.optimize. Default is sqrt(.Machine$double.eps), about 1e-8.
abstol	Float. In Julia mode with Brent method : abstol of Optim.optimize. Default is .Machine$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 mode.
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 in Julia mode) containing every information about convergence results.

Examples

data <- rep(100, 5)

simulatorQ <- function(Theta, quantiles){
qpois(quantiles, lambda = Theta)
}
dsumstats <- function(simulations, data){
mean_simu <- mean(rowMeans(simulations))
mean_data <- mean(data)
(mean_simu-mean_data)^2
}

flimoptim(5, data, dsumstats, simulatorQ,
lower = 50,
upper = 150)

flimo documentation built on May 31, 2023, 6:04 p.m.