Optimize a design defined in a PopED database using the objective function described in the database (or in the arguments to this function). The function works for both discrete and continuous optimization variables.
Usage
poped_optim(
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,
ds_index = poped.db$parameters$ds_index,
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,
mrgsolve_model = 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,
allow_replicates = TRUE,
allow_replicates_xt = TRUE,
allow_replicates_a = TRUE,
...
)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 Searchoptim_ARS.c("LS")is for Line Searchoptim_LS.c("BFGS")is for Method "L-BFGS-B" fromoptim.c("GA")is for the genetic algorithm fromga.- 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))
- ds_index
Ds_index is a vector set to 1 if a parameter is uninteresting, otherwise 0. size=(1,num unfixed parameters). First unfixed bpop, then unfixed d, then unfixed docc and last unfixed sigma. Default is the fixed effects being important, everything else not important. Used in conjunction with
ofv_calc_type=6.- 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(). Seestart_parallel.- mrgsolve_model
If the computations require a mrgsolve model and you are using the "snow" method then you need to specify the name of the model object created by
mreadormcode.- loop_methods
Should the optimization methods be looped for
iter_maxiterations, 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_maxiterations 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(ifmaximize==FALSEthen 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(ifmaximize==FALSEthen -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(ifmaximize==FALSEthen -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?
- allow_replicates
Should the algorithm allow optimized design components to have the same value? If FALSE then all discrete optimizations will not allow replicates within variable types (equivalent to
allow_replicates_xt=FALSEandallow_replicates_a=FALSE).- allow_replicates_xt
Should the algorithm allow optimized
xtdesign components to have the same value? If FALSE then all discrete optimizations will not allow replicates.- allow_replicates_a
Should the algorithm allow optimized
adesign components to have the same value? If FALSE then all discrete optimizations will not allow replicates.- ...
arguments passed to other functions.
Details
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.
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.
References
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.
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_1(),
poped_optim_2(),
poped_optim_3(),
poped_optimize()
Examples
library(PopED)
############# START #################
## Create PopED database
## (warfarin model for optimization)
#####################################
## Warfarin example from software comparison in:
## Nyberg et al., "Methods and software tools for design evaluation
## for population pharmacokinetics-pharmacodynamics studies",
## Br. J. Clin. Pharm., 2014.
## Optimization using an additive + proportional reidual error
## to avoid sample times at very low concentrations (time 0 or very late samples).
## find the parameters that are needed to define from the structural model
ff.PK.1.comp.oral.sd.CL
#> function (model_switch, xt, parameters, poped.db)
#> {
#> with(as.list(parameters), {
#> y = xt
#> y = (DOSE * Favail * KA/(V * (KA - CL/V))) * (exp(-CL/V *
#> xt) - exp(-KA * xt))
#> return(list(y = y, poped.db = poped.db))
#> })
#> }
#> <bytecode: 0x557079188b38>
#> <environment: namespace:PopED>
## -- parameter definition function
## -- names match parameters in function ff
sfg <- function(x,a,bpop,b,bocc){
parameters=c(CL=bpop[1]*exp(b[1]),
V=bpop[2]*exp(b[2]),
KA=bpop[3]*exp(b[3]),
Favail=bpop[4],
DOSE=a[1])
return(parameters)
}
## -- Define initial design and design space
poped.db <- create.poped.database(ff_fun=ff.PK.1.comp.oral.sd.CL,
fg_fun=sfg,
fError_fun=feps.add.prop,
bpop=c(CL=0.15, V=8, KA=1.0, Favail=1),
notfixed_bpop=c(1,1,1,0),
d=c(CL=0.07, V=0.02, KA=0.6),
sigma=c(prop=0.01,add=0.25),
groupsize=32,
xt=c( 0.5,1,2,6,24,36,72,120),
minxt=0.01,
maxxt=120,
a=c(DOSE=70),
mina=c(DOSE=0.01),
maxa=c(DOSE=100))
############# END ###################
## Create PopED database
## (warfarin model for optimization)
#####################################
##############
# D-family Optimization
##############
# below are a number of ways to optimize the problem
# ARS+BFGS+LS optimization of dose
# optimization with just a few iterations
# only to check that things are working
out_1 <- poped_optim(poped.db,opt_a =TRUE,
control = list(ARS=list(iter=2),
BFGS=list(maxit=2),
LS=list(line_length=2)),
iter_max = 1)
#> ===============================================================================
#> Initial design evaluation
#>
#> Initial OFV = 55.3964
#>
#> Initial design
#> expected relative standard error
#> (%RSE, rounded to nearest integer)
#> Parameter Values RSE_0
#> CL 0.15 5
#> V 8 3
#> KA 1 14
#> d_CL 0.07 30
#> d_V 0.02 37
#> d_KA 0.6 27
#> sig_prop 0.01 32
#> sig_add 0.25 26
#>
#> ==============================================================================
#> Optimization of design parameters
#>
#> * Optimize Covariates
#>
#> ************* Iteration 1 for all optimization methods***********************
#>
#> *******************************************
#> Running Adaptive Random Search Optimization
#> *******************************************
#> Initial OFV = 55.3964
#>
#> Total iterations: 2
#> Elapsed time: 0.014 seconds.
#>
#> Final OFV = 55.39645
#> Parameters: 70
#>
#> *******************************************
#> Running BFGS Optimization
#> *******************************************
#> initial value -55.396450
#> final value -55.766379
#> stopped after 2 iterations
#>
#> *******************************************
#> Running Line Search Optimization
#> *******************************************
#>
#> Initial parameters: 83.20112
#> Initial OFV: 55.76638
#>
#> Searching parameter 1
#> Changed from 83.2011 to 100 ; OFV = 56.032
#>
#> Elapsed time: 0.024 seconds.
#>
#> Final OFV = 56.03204
#> Parameters: 100
#>
#> *******************************************
#> Stopping criteria testing
#> (Compare between start of iteration and end of iteration)
#> *******************************************
#> Difference in OFV: 0.636
#> Relative difference in OFV: 1.15%
#> Efficiency:
#> ((exp(ofv_final) / exp(ofv_init))^(1/n_parameters)) = 1.0827
#>
#> Efficiency stopping criteria:
#> Is (1.0827 <= 1.001)? No.
#> Stopping criteria NOT achieved.
#>
#> Stopping criteria NOT achieved.
#>
#> ===============================================================================
#> FINAL RESULTS
#>
#> Optimized Covariates:
#> Group 1: 100
#>
#> OFV = 56.032
#>
#> Efficiency:
#> ((exp(ofv_final) / exp(ofv_init))^(1/n_parameters)) = 1.0827
#>
#> Expected relative standard error
#> (%RSE, rounded to nearest integer):
#> Parameter Values RSE_0 RSE
#> CL 0.15 5 5
#> V 8 3 3
#> KA 1 14 14
#> d_CL 0.07 30 28
#> d_V 0.02 37 34
#> d_KA 0.6 27 26
#> sig_prop 0.01 32 23
#> sig_add 0.25 26 30
#>
#> Total running time: 0.226 seconds
# cost function
# PRED at 120 hours
crit_fcn <- function(poped.db,...){
pred_df <- model_prediction(poped.db)
return(pred_df[pred_df$Time==120,"PRED"])
}
# maximize cost function
out_2 <- poped_optim(poped.db,opt_a =TRUE,
ofv_fun=crit_fcn,
control = list(ARS=list(iter=2),
BFGS=list(maxit=2),
LS=list(line_length=2)),
iter_max = 2)
#> ===============================================================================
#> Initial design evaluation
#>
#> Initial OFV = 0.939866
#>
#> Initial design
#> expected relative standard error
#> (%RSE, rounded to nearest integer)
#> Parameter Values RSE_0
#> CL 0.15 5
#> V 8 3
#> KA 1 14
#> d_CL 0.07 30
#> d_V 0.02 37
#> d_KA 0.6 27
#> sig_prop 0.01 32
#> sig_add 0.25 26
#>
#> ==============================================================================
#> Optimization of design parameters
#>
#> * Optimize Covariates
#>
#> ************* Iteration 1 for all optimization methods***********************
#>
#> *******************************************
#> Running Adaptive Random Search Optimization
#> *******************************************
#> Initial OFV = 0.939866
#>
#> Total iterations: 2
#> Elapsed time: 0.006 seconds.
#>
#> Final OFV = 0.9398657
#> Parameters: 70
#>
#> *******************************************
#> Running BFGS Optimization
#> *******************************************
#> initial value -0.939866
#> final value -1.014649
#> stopped after 2 iterations
#>
#> *******************************************
#> Running Line Search Optimization
#> *******************************************
#>
#> Initial parameters: 75.56977
#> Initial OFV: 1.014649
#>
#> Searching parameter 1
#> Changed from 75.5698 to 100 ; OFV = 1.34267
#>
#> Elapsed time: 0.008 seconds.
#>
#> Final OFV = 1.342665
#> Parameters: 100
#>
#> *******************************************
#> Stopping criteria testing
#> (Compare between start of iteration and end of iteration)
#> *******************************************
#> Difference in OFV: 0.403
#> Relative difference in OFV: 42.9%
#> Efficiency:
#> (ofv_final / ofv_init) = 1.4286
#>
#> Efficiency stopping criteria:
#> Is (1.4286 <= 1.001)? No.
#> Stopping criteria NOT achieved.
#>
#> Stopping criteria NOT achieved.
#>
#> ************* Iteration 2 for all optimization methods***********************
#>
#> *******************************************
#> Running Adaptive Random Search Optimization
#> *******************************************
#> Initial OFV = 1.34267
#>
#> Total iterations: 2
#> Elapsed time: 0.01 seconds.
#>
#> Final OFV = 1.342665
#> Parameters: 100
#>
#> *******************************************
#> Running BFGS Optimization
#> *******************************************
#> initial value -1.342665
#> final value -1.342665
#> converged
#>
#> *******************************************
#> Running Line Search Optimization
#> *******************************************
#>
#> Initial parameters: 100
#> Initial OFV: 1.342665
#>
#> Searching parameter 1
#> Changed from 100 to 100 ; OFV = 1.34267
#>
#> Elapsed time: 0.009 seconds.
#>
#> Final OFV = 1.342665
#> Parameters: 100
#>
#> *******************************************
#> Stopping criteria testing
#> (Compare between start of iteration and end of iteration)
#> *******************************************
#> Difference in OFV: 0
#> Relative difference in OFV: 0%
#> Efficiency:
#> (ofv_final / ofv_init) = 1
#>
#> Efficiency stopping criteria:
#> Is (1 <= 1.001)? Yes.
#> Stopping criteria achieved.
#>
#> Stopping criteria achieved.
#>
#> ===============================================================================
#> FINAL RESULTS
#>
#> Optimized Covariates:
#> Group 1: 100
#>
#> OFV = 1.34267
#>
#> Efficiency:
#> (ofv_final / ofv_init) = 1.4286
#>
#> Expected relative standard error
#> (%RSE, rounded to nearest integer):
#> Parameter Values RSE_0 RSE
#> CL 0.15 5 5
#> V 8 3 3
#> KA 1 14 14
#> d_CL 0.07 30 28
#> d_V 0.02 37 34
#> d_KA 0.6 27 26
#> sig_prop 0.01 32 23
#> sig_add 0.25 26 30
#>
#> Total running time: 0.334 seconds
# minimize the cost function
out_3 <- poped_optim(poped.db,opt_a =TRUE,
ofv_fun=crit_fcn,
control = list(ARS=list(iter=2),
BFGS=list(maxit=2),
LS=list(line_length=2)),
iter_max = 2,
maximize = FALSE,
evaluate_fim = FALSE)
#> ===============================================================================
#> Initial design evaluation
#>
#> Initial OFV = 0.939866
#> ==============================================================================
#> Optimization of design parameters
#>
#> * Optimize Covariates
#>
#> ************* Iteration 1 for all optimization methods***********************
#>
#> *******************************************
#> Running Adaptive Random Search Optimization
#> *******************************************
#> Initial OFV = 0.939866
#>
#> Total iterations: 2
#> Elapsed time: 0.006 seconds.
#>
#> Final OFV = 0.9398657
#> Parameters: 70
#>
#> *******************************************
#> Running BFGS Optimization
#> *******************************************
#> initial value 0.939866
#> final value 0.856208
#> stopped after 2 iterations
#>
#> *******************************************
#> Running Line Search Optimization
#> *******************************************
#>
#> Initial parameters: 63.76928
#> Initial OFV: 0.856208
#>
#> Searching parameter 1
#> Changed from 63.7693 to 0.01 ; OFV = 0.000134267
#>
#> Elapsed time: 0.008 seconds.
#>
#> Final OFV = 0.0001342665
#> Parameters: 0.01
#>
#> *******************************************
#> Stopping criteria testing
#> (Compare between start of iteration and end of iteration)
#> *******************************************
#> Difference in OFV: -0.94
#> Relative difference in OFV: -100%
#> Efficiency:
#> (ofv_final / ofv_init) = 0.00014286
#>
#> Efficiency stopping criteria:
#> Is (0.00014286 >= 0.999)? No.
#> Stopping criteria NOT achieved.
#>
#> Stopping criteria NOT achieved.
#>
#> ************* Iteration 2 for all optimization methods***********************
#>
#> *******************************************
#> Running Adaptive Random Search Optimization
#> *******************************************
#> Initial OFV = 0.000134267
#>
#> Total iterations: 2
#> Elapsed time: 0.006 seconds.
#>
#> Final OFV = 0.0001342665
#> Parameters: 0.01
#>
#> *******************************************
#> Running BFGS Optimization
#> *******************************************
#> initial value 0.000134
#> final value 0.000134
#> converged
#>
#> *******************************************
#> Running Line Search Optimization
#> *******************************************
#>
#> Initial parameters: 0.01000001
#> Initial OFV: 0.0001342667
#>
#> Searching parameter 1
#> Changed from 0.01 to 0.01 ; OFV = 0.000134267
#>
#> Elapsed time: 0.008 seconds.
#>
#> Final OFV = 0.0001342665
#> Parameters: 0.01
#>
#> *******************************************
#> Stopping criteria testing
#> (Compare between start of iteration and end of iteration)
#> *******************************************
#> Difference in OFV: 0
#> Relative difference in OFV: 0%
#> Efficiency:
#> (ofv_final / ofv_init) = 1
#>
#> Efficiency stopping criteria:
#> Is (1 >= 0.999)? Yes.
#> Stopping criteria achieved.
#>
#> Stopping criteria achieved.
#>
#> ===============================================================================
#> FINAL RESULTS
#>
#> Optimized Covariates:
#> Group 1: 0.01
#>
#> OFV = 0.000134267
#>
#> Efficiency:
#> (ofv_final / ofv_init) = 0.00014286
#>
#> Total running time: 0.323 seconds
if (FALSE) { # \dontrun{
# RS+BFGS+LS optimization of sample times
# (longer run time than above but more likely to reach a maximum)
output <- poped_optim(poped.db,opt_xt=T,parallel = TRUE)
get_rse(output$FIM,output$poped.db)
plot_model_prediction(output$poped.db)
# optimization with only integer times allowed
poped.db.2 <- poped.db
poped.db.2$design_space$xt_space <- matrix(list(seq(1,120)),1,8)
output_2 <- poped_optim(poped.db.2,opt_xt=T,parallel = TRUE)
get_rse(output_2$FIM,output_2$poped.db)
plot_model_prediction(output_2$poped.db)
# Examine efficiency of sampling windows
plot_efficiency_of_windows(output_2$poped.db,xt_windows=0.5)
plot_efficiency_of_windows(output_2$poped.db,xt_windows=1)
# Adaptive Random Search (ARS, just a few samples here)
rs.output <- poped_optim(poped.db,opt_xt=T,method = "ARS",
control = list(ARS=list(iter=5)))
get_rse(rs.output$FIM,rs.output$poped.db)
# line search, DOSE and sample time optimization
ls.output <- poped_optim(poped.db,opt_xt=T,opt_a=T,method = "LS",
control = list(LS=list(line_length=5)))
# Adaptive random search,
# DOSE and sample time optimization
ars.output <- poped_optim(poped.db,opt_xt=T,opt_a=T,method = "ARS",
control = list(ARS=list(iter=5)))
# BFGS gradient search from the stats::optim() function,
# DOSE and sample time optimization
bfgs.output <- poped_optim(poped.db,opt_xt=T,opt_a=T,method = "BFGS",
control = list(BFGS=list(maxit=5)))
# genetic algorithm from the GA::ga() function,
# DOSE and sample time optimization
ga.output <- poped_optim(poped.db,opt_xt=T,opt_a=F,method = "GA",parallel=T)
# cost function with GA
# maximize
out_2 <- poped_optim(poped.db,opt_a =TRUE,
ofv_fun=crit_fcn,
parallel = T,
method=c("GA"))
# cost function with GA
# minimize
out_2 <- poped_optim(poped.db,opt_a =TRUE,
ofv_fun=crit_fcn,
parallel = T,
method=c("GA"),
iter_max = 1,
maximize = F,
evaluate_fim = F)
# optimize distribution of individuals in 3 groups
poped_db_2 <- create.poped.database(
ff_fun=ff.PK.1.comp.oral.sd.CL,
fg_fun=sfg,
fError_fun=feps.add.prop,
bpop=c(CL=0.15, V=8, KA=1.0, Favail=1),
notfixed_bpop=c(1,1,1,0),
d=c(CL=0.07, V=0.02, KA=0.6),
sigma=c(prop=0.01,add=0.25),
groupsize=32,
m=3,
xt=list(c( 0.5,1,2,6,8),c(36,72,120),
c(10,12,14,16,18,20,22,24)),
minxt=0.01,
maxxt=120,
a=c(DOSE=70),
mina=c(DOSE=0.01),
maxa=c(DOSE=100))
opt_xt_inds <-
poped_optim(poped_db_2,
opt_a =TRUE,
opt_inds = TRUE,
control = list(ARS=list(iter=2),
BFGS=list(maxit=2),
LS=list(line_length=2)),
iter_max = 1)
##############
# E-family Optimization
##############
# Adding 10% log-normal Uncertainty to fixed effects (not Favail)
bpop_vals <- c(CL=0.15, V=8, KA=1.0, Favail=1)
bpop_vals_ed_ln <- cbind(ones(length(bpop_vals),1)*4, # log-normal distribution
bpop_vals,
ones(length(bpop_vals),1)*(bpop_vals*0.1)^2) # 10% of bpop value
bpop_vals_ed_ln["Favail",] <- c(0,1,0)
bpop_vals_ed_ln
## -- Define initial design and design space
poped.db <- create.poped.database(
ff_fun=ff.PK.1.comp.oral.sd.CL,
fg_fun=sfg,
fError_fun=feps.add.prop,
bpop=bpop_vals_ed_ln,
notfixed_bpop=c(1,1,1,0),
d=c(CL=0.07, V=0.02, KA=0.6),
sigma=c(0.01,0.25),
groupsize=32,
xt=c( 0.5,1,2,6,24,36,72,120),
minxt=0,
maxxt=120,
a=70,
mina=0,
maxa=100)
# E_ln(D) optimization using Random search (just a few samples here)
output <- poped_optim(poped.db,opt_xt=TRUE,opt_a=TRUE,d_switch=0,
method = c("ARS","LS"),
control = list(ARS=list(iter=2),
LS=list(line_length=2)),
iter_max = 1)
get_rse(output$FIM,output$poped.db)
# ED with laplace approximation,
# optimization using Random search (just a few iterations here)
ars.output <- poped_optim(poped.db,opt_xt=T,opt_a=T,method = "ARS",
d_switch=0,use_laplace=TRUE,#laplace.fim=TRUE,
parallel=T,
control = list(ARS=list(iter=5)))
} # }