Create some output to the screen and a text file that summarizes the optimization settings you will use to optimize.
Arguments
- fn
The file handle to write to.
- poped.db
A PopED database.
- opt_method
If "RS" (random search), "SG" (stochastic gradient) or "DO" (discrete optimization) then specific output is produced.
See also
Other Helper:
blockexp(),
blockfinal(),
blockheader()
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: 0x564e4be77e58>
#> <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)
#####################################
blockopt(fn="",poped.db,opt_method="SG")
#> ==============================================================================
#> Optimization Settings
#>
#> Stochastic Gradient :
#> Maximum number of cycles : 150
#> Epsilon for termination : 1e-08
#> First step factor for xt: 0.001
#> First step factor for a: 0.001
#> RS m0it: 50
#>
#> NULL
blockopt(fn="",poped.db,opt_method="RS")
#> ==============================================================================
#> Optimization Settings
#>
#> Random Search :
#> Number of cycles : 300
#> Locality factor for xt : 10
#> Locality factor for a : 10
#>
#> NULL
blockopt(fn="",poped.db,opt_method="DO")
#> ==============================================================================
#> Optimization Settings
#>
#> Discrete Optimization :
#> RS int it: 250
#> SG int it: 50
#>
#> NULL