This is a structural model function that encodes a model that is one-compartment, oral absorption, multiple bolus dose, parameterized using KE. The function is suitable for input to the create.poped.database function using the ff_file argument.

ff.PK.1.comp.oral.md.KE(model_switch, xt, parameters, poped.db)

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.

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:

  1. y the values of the model at the specified points.

  2. poped.db A (potentially modified) poped database.

See also

Examples

library(PopED) ## find the parameters that are needed to define in the structural model ff.PK.1.comp.oral.md.KE
#> function (model_switch, xt, parameters, poped.db) #> { #> with(as.list(parameters), { #> y = xt #> N = floor(xt/TAU) + 1 #> y = (DOSE * Favail/V) * (KA/(KA - KE)) * (exp(-KE * (xt - #> (N - 1) * TAU)) * (1 - exp(-N * KE * TAU))/(1 - exp(-KE * #> TAU)) - exp(-KA * (xt - (N - 1) * TAU)) * (1 - exp(-N * #> KA * TAU))/(1 - exp(-KA * TAU))) #> return(list(y = y, poped.db = poped.db)) #> }) #> } #> <bytecode: 0x7fe20d7bf9d0> #> <environment: namespace:PopED>
## -- parameter definition function ## -- names match parameters in function ff sfg <- function(x,a,bpop,b,bocc){ ## -- parameter definition function parameters=c( V=bpop[1]*exp(b[1]), KA=bpop[2]*exp(b[2]), KE=bpop[3]*exp(b[3]), Favail=bpop[4], DOSE=a[1], TAU=a[2]) return( parameters ) } ## -- Define design and design space poped.db <- create.poped.database(ff_fun=ff.PK.1.comp.oral.md.KE, fg_fun=sfg, fError_fun=feps.add.prop, groupsize=20, m=2, sigma=c(0.04,5e-6), bpop=c(V=72.8,KA=0.25,KE=3.75/72.8,Favail=0.9), d=c(V=0.09,KA=0.09,KE=0.25^2), notfixed_bpop=c(1,1,1,0), notfixed_sigma=c(0,0), xt=c( 1,2,8,240,245), minxt=c(0,0,0,240,240), maxxt=c(10,10,10,248,248), a=cbind(c(20,40),c(24,24)), bUseGrouped_xt=1, maxa=c(200,40), mina=c(0,2)) ## 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,] 0.06138421 -6.369454 34.30738 0.0000 0.0000 0.0000 #> [2,] -6.36945403 3667.860710 9887.52379 0.0000 0.0000 0.0000 #> [3,] 34.30737724 9887.523789 145132.13111 0.0000 0.0000 0.0000 #> [4,] 0.00000000 0.000000 0.00000 1322.9667 167.9800 206.8939 #> [5,] 0.00000000 0.000000 0.00000 167.9800 656.8986 202.6588 #> [6,] 0.00000000 0.000000 0.00000 206.8939 202.6588 1853.6889
det(FIM)
#> [1] 1.810534e+16
get_rse(FIM,poped.db)
#> V KA KE d_V d_KA d_KE #> 8.215338 10.090955 7.566975 31.220520 44.677836 38.005067