poped_optim_1: Optimization main module for PopED Optimize the objective...

In PopED: Population (and Individual) Optimal Experimental Design

Optimization main module for PopED Optimize the objective function. The function works for both discrete and continuous optimization variables. If more than one optimization method is specified then the methods are run in series. If loop_methods=TRUE then the series of optimization methods will be run for iter_max iterations, or until the efficiency of the design after the current series (compared to the start of the series) is less than stop_crit_eff.

Description

This function takes information from the PopED database supplied as an argument. The PopED database supplies information about the the model, parameters, design and methods to use. Some of the arguments coming from the PopED database can be overwritten; if they are supplied then they are used instead of the arguments from the PopED database.

Usage

poped_optim_1(
  poped.db,
  opt_xt = poped.db$settings$optsw[2],
  opt_a = poped.db$settings$optsw[4],
  opt_x = poped.db$settings$optsw[3],
  opt_samps = poped.db$settings$optsw[1],
  opt_inds = poped.db$settings$optsw[5],
  method = c("ARS", "BFGS", "LS"),
  control = list(),
  trace = TRUE,
  fim.calc.type = poped.db$settings$iFIMCalculationType,
  ofv_calc_type = poped.db$settings$ofv_calc_type,
  approx_type = poped.db$settings$iApproximationMethod,
  d_switch = poped.db$settings$d_switch,
  ED_samp_size = poped.db$settings$ED_samp_size,
  bLHS = poped.db$settings$bLHS,
  use_laplace = poped.db$settings$iEDCalculationType,
  out_file = "",
  parallel = F,
  parallel_type = NULL,
  num_cores = NULL,
  loop_methods = ifelse(length(method) > 1, TRUE, FALSE),
  iter_max = 10,
  stop_crit_eff = 1.001,
  stop_crit_diff = NULL,
  stop_crit_rel = NULL,
  ofv_fun = poped.db$settings$ofv_fun,
  maximize = T,
  ...
)

Arguments

poped.db	A PopED database.
opt_xt	Should the sample times be optimized?
opt_a	Should the continuous design variables be optimized?
opt_x	Should the discrete design variables be optimized?
opt_samps	Are the number of sample times per group being optimized?
opt_inds	Are the number of individuals per group being optimized?
method	A vector of optimization methods to use in a sequential fashion. Options are c("ARS","BFGS","LS","GA"). c("ARS") is for Adaptive Random Search optim_ARS. c("LS") is for Line Search optim_LS. c("BFGS") is for Method "L-BFGS-B" from optim. c("GA") is for the genetic algorithm from ga.
control	Contains control arguments for each method specified.
trace	Should the algorithm output results intermittently.
fim.calc.type	The method used for calculating the FIM. Potential values: 0 = Full FIM. No assumption that fixed and random effects are uncorrelated. 1 = Reduced FIM. Assume that there is no correlation in the FIM between the fixed and random effects, and set these elements in the FIM to zero. 2 = weighted models (placeholder). 3 = Not currently used. 4 = Reduced FIM and computing all derivatives with respect to the standard deviation of the residual unexplained variation (sqrt(SIGMA) in NONMEM). This matches what is done in PFIM, and assumes that the standard deviation of the residual unexplained variation is the estimated parameter (NOTE: NONMEM estimates the variance of the residual unexplained variation by default). 5 = Full FIM parameterized with A,B,C matrices & derivative of variance. 6 = Calculate one model switch at a time, good for large matrices. 7 = Reduced FIM parameterized with A,B,C matrices & derivative of variance.
ofv_calc_type	OFV calculation type for FIM 1 = "D-optimality". Determinant of the FIM: det(FIM) 2 = "A-optimality". Inverse of the sum of the expected parameter variances: 1/trace_matrix(inv(FIM)) 4 = "lnD-optimality". Natural logarithm of the determinant of the FIM: log(det(FIM)) 6 = "Ds-optimality". Ratio of the Determinant of the FIM and the Determinant of the uninteresting rows and columns of the FIM: det(FIM)/det(FIM_u) 7 = Inverse of the sum of the expected parameter RSE: 1/sum(get_rse(FIM,poped.db,use_percent=FALSE))
approx_type	Approximation method for model, 0=FO, 1=FOCE, 2=FOCEI, 3=FOI.
d_switch	******START OF CRITERION SPECIFICATION OPTIONS********** D-family design (1) or ED-family design (0) (with or without parameter uncertainty)
ED_samp_size	Sample size for E-family sampling
bLHS	How to sample from distributions in E-family calculations. 0=Random Sampling, 1=LatinHyperCube –
use_laplace	Should the Laplace method be used in calculating the expectation of the OFV?
out_file	Save output from the optimization to a file.
parallel	Should we use parallel computations?
parallel_type	Which type of parallelization should be used? Can be "snow" or "multicore". "snow" works on Linux-like systems & Windows. "multicore" works only on Linux-like systems. By default this is chosen for you depending on your operating system. See start_parallel.
num_cores	The number of cores to use in the parallelization. By default is set to the number output from parallel::detectCores(). See start_parallel.
loop_methods	Should the optimization methods be looped for iter_max iterations, or until the efficiency of the design after the current series (compared to the start of the series) is less than, or equal to, stop_crit_eff?
iter_max	If line search is used then the algorithm tests if line search (always run at the end of the optimization iteration) changes the design in any way. If not, the algorithm stops. If yes, then a new iteration is run unless iter_max iterations have already been run.
stop_crit_eff	If loop_methods==TRUE, the looping will stop if the efficiency of the design after the current series (compared to the start of the series) is less than, or equal to, stop_crit_eff (if maximize==FALSE then 1/stop_crit_eff is the cut off and the efficiency must be greater than or equal to this value to stop the looping).
stop_crit_diff	If loop_methods==TRUE, the looping will stop if the difference in criterion value of the design after the current series (compared to the start of the series) is less than, or equal to, stop_crit_diff (if maximize==FALSE then -stop_crit_diff is the cut off and the difference in criterion value must be greater than or equal to this value to stop the looping).
stop_crit_rel	If loop_methods==TRUE, the looping will stop if the relative difference in criterion value of the design after the current series (compared to the start of the series) is less than, or equal to, stop_crit_rel (if maximize==FALSE then -stop_crit_rel is the cut off and the relative difference in criterion value must be greater than or equal to this value to stop the looping).
ofv_fun	User defined function used to compute the objective function. The function must have a poped database object as its first argument and have "..." in its argument list. Can be referenced as a function or as a file name where the function defined in the file has the same name as the file. e.g. "cost.txt" has a function named "cost" in it.
maximize	Should the objective function be maximized or minimized?
...	arguments passed to other functions.

References

1. M. Foracchia, A.C. Hooker, P. Vicini and A. Ruggeri, "PopED, a software fir optimal experimental design in population kinetics", Computer Methods and Programs in Biomedicine, 74, 2004.

2. J. Nyberg, S. Ueckert, E.A. Stroemberg, S. Hennig, M.O. Karlsson and A.C. Hooker, "PopED: An extended, parallelized, nonlinear mixed effects models optimal design tool", Computer Methods and Programs in Biomedicine, 108, 2012.

See Also

Other Optimize: Doptim(), LEDoptim(), RS_opt(), a_line_search(), bfgsb_min(), calc_autofocus(), calc_ofv_and_grad(), mfea(), optim_ARS(), optim_LS(), poped_optim(), poped_optim_2(), poped_optim_3(), poped_optimize()

PopED documentation built on Oct. 8, 2024, 1:07 a.m.