Create some output to the screen and a text file that summarizes the problem you solved.
Usage
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,
...
)Arguments
- 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:
column 1 the type of the distribution for E-family designs (0 = Fixed, 1 = Normal, 2 = Uniform, 3 = User Defined Distribution, 4 = lognormal and 5 = truncated normal)
column 2 defines the mean.
column 3 defines the variance of the distribution (or length of uniform distribution).
Can also just supply the parameter values as a vector
c()if no uncertainty around the parameter value is to be used. The parameter order of 'bpop' is defined in the 'fg_fun' or 'fg_file'. If you use named arguments in 'bpop' then the order of this vector can be rearranged to match the 'fg_fun' or 'fg_file'. See `reorder_parameter_vectors`.- 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
c(). The parameter order of 'd' is defined in the 'fg_fun' or 'fg_file'. If you use named arguments in 'd' then the order of this vector can be rearranged to match the 'fg_fun' or 'fg_file'. See `reorder_parameter_vectors`.- 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
c().- 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
fileor""will output to the screen.- trflag
Should the optimization be output to the screen and to a file?
Should the footer text be printed out?
- ...
arguments passed to
evaluate.fimandofv_fim.
See also
Other Helper:
blockexp(),
blockheader(),
blockopt()
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)
#####################################
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))
#> ===============================================================================
#> FINAL RESULTS
#> Optimized Sampling Schedule
#> Group 1: 0.5 1 2 6 24 36 72 120
#>
#> OFV = 1.14386e+24
#>
#> Efficiency:
#> ((exp(ofv_final) / exp(ofv_init))^(1/n_parameters)) = NaN
#>
#> Expected relative standard error
#> (%RSE, rounded to nearest integer):
#> Parameter Values RSE_0 RSE
#> CL 0.15 5 5
#> V 8 3 3
#> KA 1 14 14
#> d_CL 0.07 30 30
#> d_V 0.02 37 37
#> d_KA 0.6 27 27
#> sig_prop 0.01 32 32
#> sig_add 0.25 26 26
#>
#> Total running time: 1.557 seconds