ed_mftot | R Documentation |
Compute the expectation of the FIM given the model, parameters, distributions of parameter uncertainty, design and methods defined in the PopED database.
ed_mftot(
model_switch,
groupsize,
ni,
xtoptn,
xoptn,
aoptn,
bpopdescr,
ddescr,
covd,
sigma,
docc,
poped.db,
calc_fim = TRUE,
...
)
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. |
xtoptn |
The xtoptn value |
xoptn |
The xoptn |
aoptn |
The aoptn value |
bpopdescr |
Matrix defining the fixed effects, per row (row number = parameter_number) we should have:
|
ddescr |
Matrix defining the diagonals of the IIV (same logic as for
the |
covd |
Column major vector defining the covariances of the IIV variances.
That is, from your full IIV matrix |
sigma |
Matrix defining the variances can covariances of the residual variability terms of the model.
can also just supply the diagonal parameter values (variances) as a |
docc |
Matrix defining the IOV, the IOV variances and the IOV distribution as for d and bpop. |
poped.db |
A PopED database. |
calc_fim |
Should the FIM be calculated or should we just use the user defined ed_penalty_pointer. |
... |
Other arguments passed to the function. |
A list containing the E(FIM) and E(OFV(FIM)) and the a poped.db.
Other FIM:
LinMatrixH()
,
LinMatrixLH()
,
LinMatrixL_occ()
,
calc_ofv_and_fim()
,
ed_laplace_ofv()
,
efficiency()
,
evaluate.e.ofv.fim()
,
evaluate.fim()
,
gradf_eps()
,
mf3()
,
mf7()
,
mftot()
,
ofv_criterion()
,
ofv_fim()
Other E-family:
calc_ofv_and_fim()
,
ed_laplace_ofv()
,
evaluate.e.ofv.fim()
library(PopED)
############# START #################
## Create PopED database
## (warfarin model for optimization
## with parameter uncertainty)
#####################################
## 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 samoples).
## find the parameters that are needed to define from the structural model
ff.PK.1.comp.oral.sd.CL
## -- 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)
}
# Adding 10% log-normal Uncertainty to fixed effects (not Favail)
bpop_vals <- c(CL=0.15, V=8, KA=1.0, Favail=1)
bpop_vals_ed_ln <- cbind(ones(length(bpop_vals),1)*4, # log-normal distribution
bpop_vals,
ones(length(bpop_vals),1)*(bpop_vals*0.1)^2) # 10% of bpop value
bpop_vals_ed_ln["Favail",] <- c(0,1,0)
bpop_vals_ed_ln
## -- 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=bpop_vals_ed_ln,
notfixed_bpop=c(1,1,1,0),
d=c(CL=0.07, V=0.02, KA=0.6),
sigma=c(0.01,0.25),
groupsize=32,
xt=c( 0.5,1,2,6,24,36,72,120),
minxt=0,
maxxt=120,
a=70,
mina=0,
maxa=100)
############# END ###################
## Create PopED database
## (warfarin model for optimization
## with parameter uncertainty)
#####################################
# very few samples
poped.db$settings$ED_samp_size=10
ed_mftot(model_switch=poped.db$design$model_switch,
groupsize=poped.db$design$groupsize,
ni=poped.db$design$ni,
xtoptn=poped.db$design$xt,
xoptn=poped.db$design$x,
aoptn=poped.db$design$a,
bpopdescr=poped.db$parameters$bpop,
ddescr=poped.db$parameters$d,
covd=poped.db$parameters$covd,
sigma=poped.db$parameters$sigma,
docc=poped.db$parameters$docc,
poped.db)["ED_ofv"]
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