tests/testthat/examples_fcn_doc/examples_poped_optim.R

In andrewhooker/PopED: Population (and Individual) Optimal Experimental Design

##############
# 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)


# 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)

# 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)


\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)))
  
}