PopED (version 0.1.0)

blockexp: Summarize your experiment for optimization routines

Description

Create some output to the screen and a text file that summarizes the initial design and the design space you will use to optimize.

Usage

blockexp(fn, poped.db, e_flag = FALSE, opt_xt = poped.db$optsw[2],
  opt_a = poped.db$optsw[4], opt_x = poped.db$optsw[4],
  opt_samps = poped.db$optsw[1], opt_inds = poped.db$optsw[5])

Arguments

fn

The file handle to write to.

e_flag

Shuould output be with uncertainty around parameters?

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 nuber of sample times per group being optimized?

opt_inds

Are the nuber of individuals per group being optimized?

See Also

Other Helper: blockfinal_2; blockheader_2; blockopt_2

Examples

## 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 samoples).
library(PopED)

## 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_file="ff.PK.1.comp.oral.sd.CL",
                                  fg_file="sfg",
                                  fError_file="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(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)
# warfarin optimization model 

blockexp("",poped.db, opt_xt=TRUE)