Optimize the objective function using a modified Federov exchange algorithm. The function works for continuous and discrete optimization variables. This function takes information from the PopED database supplied as an argument. The PopED database supplies information about the the model, parameters, design and methods to use. Some of the arguments coming from the PopED database can be overwritten; if they are supplied then they are used instead of the arguments from the PopED database.
mfea(poped.db, model_switch, ni, xt, x, a, bpopdescr, ddescr, maxxt, minxt,
maxa, mina, fmf, dmf, EAStepSize = poped.db$settings$EAStepSize,
ourzero = poped.db$settings$ourzero, opt_xt = poped.db$settings$optsw[2],
opt_a = poped.db$settings$optsw[4], opt_x = poped.db$settings$optsw[3],
trflag = T, ...)A PopED database.
A matrix that is the same size as xt, specifying which model each sample belongs to.
A vector of the number of samples in each group.
A matrix of sample times. Each row is a vector of sample times for a group.
A matrix for the discrete design variables. Each row is a group.
A matrix of covariates. Each row is a group.
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).
Matrix defining the diagnonals of the IIV (same logic as for
the bpopdescr).
Matrix or single value defining the maximum value for each xt sample. If a single value is supplied then all xt values are given the same maximum value.
Matrix or single value defining the minimum value for each xt sample. If a single value is supplied then all xt values are given the same minimum value
Vector defining the max value for each covariate. If a single value is supplied then all a values are given the same max value
Vector defining the min value for each covariate. If a single value is supplied then all a values are given the same max value
The initial value of the FIM. If set to zero then it is computed.
The inital OFV. If set to zero then it is computed.
Exchange Algorithm StepSize
Value to interpret as zero in design
Should the sample times be optimized?
Should the continuous design variables be optimized?
Should the discrete design variables be optimized?
Should the optimization be output to the screen and to a file?
arguments passed to evaluate.fim and ofv_fim.
J. Nyberg, S. Ueckert, E.A. Stroemberg, S. Hennig, M.O. Karlsson and A.C. Hooker, "PopED: An extended, parallelized, nonlinear mixed effects models optimal design tool", Computer Methods and Programs in Biomedicine, 108, 2012.
Other Optimize: Doptim,
LEDoptim, RS_opt_gen,
RS_opt, a_line_search,
bfgsb_min, calc_autofocus,
calc_ofv_and_grad, optim_ARS,
optim_LS, poped_optimize,
poped_optim
# NOT RUN {
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_file="ff.PK.1.comp.oral.sd.CL",
fg_file="sfg",
fError_file="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(0.01,0.25),
groupsize=32,
xt=c( 0.5,1,2,6,24,36,72,120),
minxt=0.01,
maxxt=120,
a=70,
mina=0.01,
maxa=100)
############# END ###################
## Create PopED database
## (warfarin model for optimization)
#####################################
##############
# typically one will use poped_optimize
# This then calls mfea
##############
# optimization of covariate, with coarse grid
out_1 <- poped_optimize(poped.db,opt_a=1,
bUseExchangeAlgorithm=1,
EAStepSize=25)
# }
# NOT RUN {
# MFEA optimization with only integer times allowed
out_2 <- poped_optimize(poped.db,opt_xt=1,
bUseExchangeAlgorithm=1,
EAStepSize=1)
get_rse(out_2$fmf,out_2$poped.db)
plot_model_prediction(out_2$poped.db)
##############
# If you really want to you can use mfea dirtectly
##############
dsl <- downsizing_general_design(poped.db)
output <- mfea(poped.db,
model_switch=dsl$model_switch,
ni=dsl$ni,
xt=dsl$xt,
x=dsl$x,
a=dsl$a,
bpopdescr=dsl$bpop,
ddescr=dsl$d,
maxxt=dsl$maxxt,
minxt=dsl$minxt,
maxa=dsl$maxa,
mina=dsl$mina,
fmf=0,dmf=0,
EAStepSize=1,
opt_xt=1)
# }
# NOT RUN {
# }
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