Compute the FIM given specific model(s), parameters, design and methods.
Arguments
- model_switch
A matrix that is the same size as xt, specifying which model each sample belongs to.
- groupsize
A vector of the number of individuals in each group.
- ni
A vector of the number of samples in each group.
- xt
A matrix of sample times. Each row is a vector of sample times for a group.
- 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.
- d
A between subject variability matrix (OMEGA in NONMEM).
- sigma
A residual unexplained variability matrix (SIGMA in NONMEM).
- docc
A between occasion variability matrix.
- poped.db
A PopED database.
See also
For an easier function to use, please see evaluate.fim.
Other FIM:
LinMatrixH(),
LinMatrixLH(),
LinMatrixL_occ(),
calc_ofv_and_fim(),
ed_laplace_ofv(),
ed_mftot(),
efficiency(),
evaluate.e.ofv.fim(),
evaluate.fim(),
gradf_eps(),
mf3(),
mf7(),
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)
#####################################
mftot(model_switch=poped.db$design$model_switch,
groupsize=poped.db$design$groupsize,
ni=poped.db$design$ni,
xt=poped.db$design$xt,
x=poped.db$design$x,
a=poped.db$design$a,
bpop=poped.db$parameters$param.pt.val$bpop,
d=poped.db$parameters$param.pt.val$d,
sigma=poped.db$parameters$sigma,
docc=poped.db$parameters$param.pt.val$docc,
poped.db)["ret"]
#> $ret
#> [,1] [,2] [,3] [,4] [,5] [,6]
#> [1,] 17141.83891 20.838375 10.011000 0.000000e+00 0.000000 0.00000000
#> [2,] 20.83837 17.268051 -3.423641 0.000000e+00 0.000000 0.00000000
#> [3,] 10.01100 -3.423641 49.864697 0.000000e+00 0.000000 0.00000000
#> [4,] 0.00000 0.000000 0.000000 2.324341e+03 9.770352 0.03523364
#> [5,] 0.00000 0.000000 0.000000 9.770352e+00 19083.877564 11.72131703
#> [6,] 0.00000 0.000000 0.000000 3.523364e-02 11.721317 38.85137516
#> [7,] 0.00000 0.000000 0.000000 7.268410e+02 9656.158553 64.78095548
#> [8,] 0.00000 0.000000 0.000000 9.062739e+01 266.487127 2.94728469
#> [,7] [,8]
#> [1,] 0.00000 0.000000
#> [2,] 0.00000 0.000000
#> [3,] 0.00000 0.000000
#> [4,] 726.84097 90.627386
#> [5,] 9656.15855 266.487127
#> [6,] 64.78096 2.947285
#> [7,] 192840.20092 6659.569867
#> [8,] 6659.56987 475.500111
#>