Description Usage Arguments See Also Examples
Create some output to the screen and a text file that summarizes the problem you solved.
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 | blockfinal(
fn,
fmf,
dmf,
groupsize,
ni,
xt,
x,
a,
model_switch,
bpop,
d,
docc,
sigma,
poped.db,
opt_xt = poped.db$settings$optsw[2],
opt_a = poped.db$settings$optsw[4],
opt_x = poped.db$settings$optsw[3],
opt_inds = poped.db$settings$optsw[5],
fmf_init = NULL,
dmf_init = NULL,
param_cvs_init = NULL,
compute_inv = TRUE,
out_file = NULL,
trflag = TRUE,
footer_flag = TRUE,
run_time = NULL,
...
)
|
fn |
The file handle to write to. |
fmf |
The initial value of the FIM. If set to zero then it is computed. |
dmf |
The initial OFV. If set to zero then it is computed. |
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. |
model_switch |
A matrix that is the same size as xt, specifying which model each sample belongs to. |
bpop |
Matrix defining the fixed effects, per row (row number = parameter_number) we should have:
Can also just supply the parameter values as a vector |
d |
Matrix defining the diagonals of the IIV (same logic as for the fixed effects
matrix bpop to define uncertainty). One can also just supply the parameter values as a |
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 |
poped.db |
A PopED database. |
opt_xt |
Should the sample times be optimized? |
opt_a |
Should the continuous design variables be optimized? |
opt_x |
Should the discrete design variables be optimized? |
opt_inds |
Are the number of individuals per group being optimized? |
fmf_init |
Initial FIM. |
dmf_init |
Initial OFV. |
param_cvs_init |
The initial design parameter RSE values in percent. |
compute_inv |
should the inverse of the FIM be used to compute expected RSE values? Often not needed except for diagnostic purposes. |
out_file |
Which file should the output be directed to? A string, a file handle using
|
trflag |
Should the optimization be output to the screen and to a file? |
footer_flag |
Should the footer text be printed out? |
... |
arguments passed to |
Other Helper:
blockexp()
,
blockheader()
,
blockopt()
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 | 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
## -- 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)
#####################################
FIM <- evaluate.fim(poped.db)
dmf <- det(FIM)
blockfinal(fn="",fmf=FIM,
dmf=dmf,
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,
model_switch=poped.db$design$model_switch,
poped.db$parameters$param.pt.val$bpop,
poped.db$parameters$param.pt.val$d,
poped.db$parameters$docc,
poped.db$parameters$param.pt.val$sigma,
poped.db,
opt_xt=TRUE,
fmf_init=FIM,
dmf_init=dmf,
param_cvs_init=get_rse(FIM,poped.db))
|
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