The function performs a linearization of the model with respect to the residual variability and then the between subject variability. Derivative of model w.r.t. eps then eta, evaluated at eps=0 and b=b_ind.
Arguments
- model_switch
A matrix that is the same size as xt, specifying which model each sample belongs to.
- xt_ind
A vector of the individual/group sample times
- x
A matrix for the discrete design variables. Each row is a group.
- a
A matrix of covariates. Each row is a group.
- bpop
The fixed effects parameter values. Supplied as a vector.
- b_ind
vector of individual realization of the BSV terms b
- bocc_ind
Vector of individual realizations of the BOV terms bocc
- NumEPS
The number of eps() terms in the model.
- poped.db
A PopED database.
See also
Other FIM:
LinMatrixH(),
LinMatrixL_occ(),
calc_ofv_and_fim(),
ed_laplace_ofv(),
ed_mftot(),
efficiency(),
evaluate.e.ofv.fim(),
evaluate.fim(),
gradf_eps(),
mf3(),
mf7(),
mftot(),
ofv_criterion(),
ofv_fim()
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: 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 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)
#####################################
#for the FOI approximation
ind=1
poped.db$settings$iApproximationMethod=3 # FOI approximation method
LinMatrixLH(model_switch=t(poped.db$design$model_switch[ind,,drop=FALSE]),
xt_ind=t(poped.db$design$xt[ind,,drop=FALSE]),
x=zeros(0,1),
a=t(poped.db$design$a[ind,,drop=FALSE]),
bpop=poped.db$parameters$bpop[,2,drop=FALSE],
b_ind=zeros(poped.db$parameters$NumRanEff,1),
bocc_ind=zeros(poped.db$parameters$NumDocc,1),
NumEPS=size(poped.db$parameters$sigma,1),
poped.db)["y"]
#> $y
#> [,1] [,2] [,3] [,4] [,5] [,6]
#> [1,] -0.01736389 0 -3.4080716 0 2.63882249 0
#> [2,] -0.05954792 0 -5.4115556 0 3.17591065 0
#> [3,] -0.18102853 0 -7.2011552 0 2.27256214 0
#> [4,] -0.74460438 0 -7.2016748 0 -0.01922018 0
#> [5,] -2.44998688 0 -3.2358671 0 -0.10864643 0
#> [6,] -2.97791125 0 -1.5623369 0 -0.08675727 0
#> [7,] -3.07661785 0 0.7649203 0 -0.04417244 0
#> [8,] -2.09673889 0 1.1568727 0 -0.01795897 0
#>