This is a residual unexplained variability (RUV) model function that encodes the model described above.
The function is suitable for input to the create.poped.database function using the
fError_file argument.
Arguments
- model_switch
a vector of values, the same size as
xt, identifying which model response should be computed for the corresponding xt value. Used for multiple response models.- xt
a vector of independent variable values (often time).
- parameters
A named list of parameter values.
- epsi
A matrix with the same number of rows as the
xtvector, columns match the numbers defined in this function.- poped.db
a poped database. This can be used to extract information that may be needed in the model file.
Value
A list consisting of:
y the values of the model at the specified points.
poped.db A (potentially modified) poped database.
See also
Other models:
feps.add(),
feps.add.prop(),
ff.PK.1.comp.oral.md.CL(),
ff.PK.1.comp.oral.md.KE(),
ff.PK.1.comp.oral.sd.CL(),
ff.PK.1.comp.oral.sd.KE(),
ff.PKPD.1.comp.oral.md.CL.imax(),
ff.PKPD.1.comp.sd.CL.emax()
Other RUV_models:
feps.add(),
feps.add.prop()
Examples
library(PopED)
############# START #################
## Create PopED database
## (warfarin example)
#####################################
## 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.
## 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: 0x557079188b38>
#> <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 model, parameters, initial design
poped.db <- create.poped.database(ff_fun=ff.PK.1.comp.oral.sd.CL,
fg_fun=sfg,
fError_fun=feps.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),
groupsize=32,
xt=c( 0.5,1,2,6,24,36,72,120),
a=c(DOSE=70))
############# END ###################
## Create PopED database
## (warfarin example)
#####################################
## create plot of model without variability
plot_model_prediction(poped.db)
## evaluate initial design
FIM <- evaluate.fim(poped.db)
FIM
#> [,1] [,2] [,3] [,4] [,5] [,6]
#> [1,] 19821.28445 -21.836551 -8.622140 0.000000e+00 0.000000 0.00000000
#> [2,] -21.83655 20.656071 -1.807099 0.000000e+00 0.000000 0.00000000
#> [3,] -8.62214 -1.807099 51.729039 0.000000e+00 0.000000 0.00000000
#> [4,] 0.00000 0.000000 0.000000 3.107768e+03 10.728786 0.02613561
#> [5,] 0.00000 0.000000 0.000000 1.072879e+01 27307.089308 3.26560786
#> [6,] 0.00000 0.000000 0.000000 2.613561e-02 3.265608 41.81083599
#> [7,] 0.00000 0.000000 0.000000 5.215403e+02 11214.210707 71.08763902
#> [,7]
#> [1,] 0.00000
#> [2,] 0.00000
#> [3,] 0.00000
#> [4,] 521.54030
#> [5,] 11214.21071
#> [6,] 71.08764
#> [7,] 806176.95068
det(FIM)
#> [1] 5.996147e+22
get_rse(FIM,poped.db)
#> CL V KA d_CL d_V d_KA sig_prop
#> 4.738266 2.756206 13.925829 25.627205 30.344316 25.777327 11.170784