Description Usage Arguments Value See Also Examples
This function computes the expected relative standard errors of a model given a design and a previously computed FIM.
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fim |
A Fisher Information Matrix (FIM). |
poped.db |
A PopED database. |
bpop |
A vector containing the values of the fixed effects used to compute the |
d |
A vector containing the values of the diagonals of the between subject variability matrix. |
docc |
Matrix defining the IOV, the IOV variances and the IOV distribution as for d and bpop. |
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 |
use_percent |
Should RSE be reported as percent? |
fim.calc.type |
The method used for calculating the FIM. Potential values:
|
prior_fim |
A prior FIM to be added to the |
... |
Additional arguments passed to |
A named list of RSE values. If the estimated parameter is assumed to be zero then for that parameter the standard error is returned.
Other evaluate_design:
evaluate.fim()
,
evaluate_design()
,
evaluate_power()
,
model_prediction()
,
plot_efficiency_of_windows()
,
plot_model_prediction()
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 | ## 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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