evaluate.fim | R Documentation |
Compute the FIM given the model, parameters, design and methods defined in the
PopED database. Some of the arguments coming from the PopED database can be overwritten;
by default these arguments are NULL
in the
function, if they are supplied then they are used instead of the arguments from the PopED database.
evaluate.fim(
poped.db,
fim.calc.type = NULL,
approx.method = NULL,
FOCE.num = NULL,
bpop.val = NULL,
d_full = NULL,
docc_full = NULL,
sigma_full = NULL,
model_switch = NULL,
ni = NULL,
xt = NULL,
x = NULL,
a = NULL,
groupsize = NULL,
deriv.type = NULL,
...
)
poped.db |
A PopED database. |
fim.calc.type |
The method used for calculating the FIM. Potential values:
|
approx.method |
Approximation method for model, 0=FO, 1=FOCE, 2=FOCEI, 3=FOI |
FOCE.num |
Number individuals in each step of FOCE approximation method |
bpop.val |
The fixed effects parameter values. Supplied as a vector. |
d_full |
A between subject variability matrix (OMEGA in NONMEM). |
docc_full |
A between occasion variability matrix. |
sigma_full |
A residual unexplained variability matrix (SIGMA in NONMEM). |
model_switch |
A matrix that is the same size as xt, specifying which model each sample belongs to. |
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. |
groupsize |
A vector of the number of individuals in each group. |
deriv.type |
A number indicating the type of derivative to use:
|
... |
Other arguments passed to the function. |
The FIM.
Other FIM:
LinMatrixH()
,
LinMatrixLH()
,
LinMatrixL_occ()
,
calc_ofv_and_fim()
,
ed_laplace_ofv()
,
ed_mftot()
,
efficiency()
,
evaluate.e.ofv.fim()
,
gradf_eps()
,
mf3()
,
mf7()
,
mftot()
,
ofv_criterion()
,
ofv_fim()
Other evaluate_design:
evaluate_design()
,
evaluate_power()
,
get_rse()
,
model_prediction()
,
plot_efficiency_of_windows()
,
plot_model_prediction()
Other evaluate_FIM:
calc_ofv_and_fim()
,
evaluate.e.ofv.fim()
,
ofv_fim()
## 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.
library(PopED)
## find the parameters that are needed to define from the structural model
ff.PK.1.comp.oral.md.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)
}
## -- 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.prop,
bpop=c(CL=0.15, V=8, KA=1.0, Favail=1),
# notfixed_bpop=c(1,1,1,0),
notfixed_bpop=c(CL=1,V=1,KA=1,Favail=0),
d=c(CL=0.07, V=0.02, KA=0.6),
sigma=0.01,
groupsize=32,
xt=c( 0.5,1,2,6,24,36,72,120),
minxt=0,
maxxt=120,
a=70)
## evaluate initial design with the reduced FIM
FIM.1 <- evaluate.fim(poped.db)
FIM.1
det(FIM.1)
det(FIM.1)^(1/7)
get_rse(FIM.1,poped.db)
## evaluate initial design with the full FIM
FIM.0 <- evaluate.fim(poped.db,fim.calc.type=0)
FIM.0
det(FIM.0)
det(FIM.0)^(1/7)
get_rse(FIM.0,poped.db)
## evaluate initial design with the reduced FIM
## computing all derivatives with respect to the
## standard deviation of the residual unexplained variation
FIM.4 <- evaluate.fim(poped.db,fim.calc.type=4)
FIM.4
det(FIM.4)
get_rse(FIM.4,poped.db,fim.calc.type=4)
## evaluate initial design with the full FIM with A,B,C matricies
## should give same answer as fim.calc.type=0
FIM.5 <- evaluate.fim(poped.db,fim.calc.type=5)
FIM.5
det(FIM.5)
get_rse(FIM.5,poped.db,fim.calc.type=5)
## evaluate initial design with the reduced FIM with
## A,B,C matricies and derivative of variance
## should give same answer as fim.calc.type=1 (default)
FIM.7 <- evaluate.fim(poped.db,fim.calc.type=7)
FIM.7
det(FIM.7)
get_rse(FIM.7,poped.db,fim.calc.type=7)
## evaluate FIM and rse with prior FIM.1
poped.db.prior = create.poped.database(poped.db, prior_fim = FIM.1)
FIM.1.prior <- evaluate.fim(poped.db.prior)
all.equal(FIM.1.prior,FIM.1) # the FIM is only computed from the design in the poped.db
get_rse(FIM.1.prior,poped.db.prior) # the RSE is computed with the prior information
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