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
## -- 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)
#####################################
if (FALSE) {
##############
# typically one will use poped_optimize
# This then calls Doptim for continuous optimization problems
##############
# RS+SG+LS optimization of sample times
# optimization with just a few iterations
# only to check that things are working
output <- poped_optimize(poped.db,opt_xt=T,
rsit=5,sgit=5,ls_step_size=5)
# RS+SG+LS optimization of sample times
# (longer run time than above but more likely to reach a maximum)
output <- poped_optimize(poped.db,opt_xt=T)
get_rse(output$fmf,output$poped.db)
plot_model_prediction(output$poped.db)
# Random search (just a few samples here)
rs.output <- poped_optimize(poped.db,opt_xt=1,opt_a=1,rsit=20,
bUseRandomSearch= 1,
bUseStochasticGradient = 0,
bUseBFGSMinimizer = 0,
bUseLineSearch = 0)
# line search, DOSE and sample time optimization
ls.output <- poped_optimize(poped.db,opt_xt=1,opt_a=1,
bUseRandomSearch= 0,
bUseStochasticGradient = 0,
bUseBFGSMinimizer = 0,
bUseLineSearch = 1,
ls_step_size=10)
# Stochastic gradient search, DOSE and sample time optimization
sg.output <- poped_optimize(poped.db,opt_xt=1,opt_a=1,
bUseRandomSearch= 0,
bUseStochasticGradient = 1,
bUseBFGSMinimizer = 0,
bUseLineSearch = 0,
sgit=20)
# BFGS search, DOSE and sample time optimization
bfgs.output <- poped_optimize(poped.db,opt_xt=1,opt_a=1,
bUseRandomSearch= 0,
bUseStochasticGradient = 0,
bUseBFGSMinimizer = 1,
bUseLineSearch = 0)
##############
# If you really want to you can use Doptim dirtectly
##############
dsl <- downsizing_general_design(poped.db)
poped.db$settings$optsw[2] <- 1 # sample time optimization
output <- Doptim(poped.db,dsl$ni, dsl$xt, dsl$model_switch, dsl$x, dsl$a,
dsl$bpop, dsl$d, dsl$maxxt, dsl$minxt,dsl$maxa,dsl$mina)
}
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