Display a summary of output from poped_optim
Usage
# S3 method for poped_optim
summary(object, ...)Arguments
- object
An object returned from
poped_optimto summarize.- ...
Additional arguments. Passed to
blockfinal.
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)
#####################################
##############
# D-family Optimization
##############
# 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: 0.022 seconds.
#>
#> Final OFV = 56.03204
#> Parameters: 100
#>
#> *******************************************
#> Running BFGS Optimization
#> *******************************************
#> initial value -56.032042
#> final value -56.032042
#> converged
#>
#> *******************************************
#> Running Line Search Optimization
#> *******************************************
#>
#> Initial parameters: 100
#> Initial OFV: 56.03204
#>
#> Searching parameter 1
#> Changed from 100 to 100 ; OFV = 56.032
#>
#> Elapsed time: 0.022 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: 0.234 seconds
summary(out_1)
#> ===============================================================================
#> 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: 0.234 seconds