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$settings$optsw[2],
opt_a = poped.db$settings$optsw[4],
opt_x = poped.db$settings$optsw[4],
opt_samps = poped.db$settings$optsw[1],
opt_inds = poped.db$settings$optsw[5]
)Arguments
- fn
The file handle to write to.
- poped.db
A PopED database.
- e_flag
Should output be with uncertainty around parameters?
- 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 number of sample times per group being optimized?
- opt_inds
Are the number of individuals per group being optimized?
See also
Other Helper:
blockfinal(),
blockheader(),
blockopt()
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)
#####################################
blockexp("",poped.db, opt_xt=TRUE)
#> ==============================================================================
#> Model description : PopED model
#>
#> Model Sizes :
#> Number of individual model parameters g[j] : Ng = 5
#> Number of population model fixed parameters bpop[j] : Nbpop = 4
#> Number of population model random effects parameters b[j] : Nb = 3
#>
#> Typical Population Parameters:
#> Warning: 5 arguments not used by format '%s[%g%s]: %5.4g %s
#> '
#> bpop[1]: 0.15
#> Warning: 5 arguments not used by format '%s[%g%s]: %5.4g %s
#> '
#> bpop[2]: 8
#> Warning: 5 arguments not used by format '%s[%g%s]: %5.4g %s
#> '
#> bpop[3]: 1
#> Warning: 5 arguments not used by format '%s[%g%s]: %5.4g %s
#> '
#> bpop[4]: 1
#>
#> Between Subject Variability matrix D (variance units)
#> 0.07 0.00 0.00
#> 0.00 0.02 0.00
#> 0.00 0.00 0.60
#>
#> Diagonal Elements of D [sqrt(param)]:
#> Warning: 5 arguments not used by format '%s[%g%s]: %5.4g %s
#> '
#> D[1,1]: 0.07 [0.2646]
#> Warning: 5 arguments not used by format '%s[%g%s]: %5.4g %s
#> '
#> D[2,2]: 0.02 [0.1414]
#> Warning: 5 arguments not used by format '%s[%g%s]: %5.4g %s
#> '
#> D[3,3]: 0.6 [0.7746]
#>
#> Residual Unexplained Variability matrix SIGMA (variance units) :
#> 0.01 0.00
#> 0.00 0.25
#>
#> Diagonal Elements of SIGMA [sqrt(param)]:
#> Warning: 5 arguments not used by format '%s[%g%s]: %5.4g %s
#> '
#> SIGMA[1,1]: 0.01 [ 0.1]
#> Warning: 5 arguments not used by format '%s[%g%s]: %5.4g %s
#> '
#> SIGMA[2,2]: 0.25 [ 0.5]
#>
#> ==============================================================================
#> Experiment description (design and design space)
#>
#> Warning: 2 arguments not used by format 'Number of individuals: %g
#> '
#> Number of individuals: 32
#> Number of groups (individuals with same design): 1
#> Number of individuals per group:
#>
#> Warning: 2 arguments not used by format ' Group %g: %g
#> '
#> Group 1: 32
#> Number of samples per group:
#> Number of discrete experimental variables: 0
#> Number of model covariates: 1
#>
#> Initial Sampling Schedule
#> Group 1: 0.5 1 2 6 24 36 72 120
#>
#> Minimum allowed sampling values
#> Group 1: 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01
#>
#> Maximum allowed sampling values
#> Group 1: 120 120 120 120 120 120 120 120
#>
#> Covariates:
#> Group 1:
#> Warning: 2 arguments not used by format '%g'
#> 70
#>
#> NULL