PopED (version 0.6.0)

mfea: Modified Fedorov Exchange Algorithm

Description

Optimize the objective function using a modified Fedorov 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.

Usage

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,
  ...
)

Arguments

poped.db

A PopED database.

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.

bpopdescr

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).

ddescr

Matrix defining the diagonals of the IIV (same logic as for the bpopdescr).

maxxt

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.

minxt

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

maxa

Vector defining the max value for each covariate. If a single value is supplied then all a values are given the same max value

mina

Vector defining the min value for each covariate. If a single value is supplied then all a values are given the same max value

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.

EAStepSize

Exchange Algorithm StepSize

ourzero

Value to interpret as zero in design

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?

trflag

Should the optimization be output to the screen and to a file?

...

arguments passed to evaluate.fim and ofv_fim.

References

1. 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.

See Also

Other Optimize: Doptim(), LEDoptim(), RS_opt(), a_line_search(), bfgsb_min(), calc_autofocus(), calc_ofv_and_grad(), optim_ARS(), optim_LS(), poped_optim_1(), poped_optim_2(), poped_optim_3(), poped_optimize(), poped_optim()

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

## -- 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)
#####################################

##############
# 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,out_file = "")


if (FALSE) {
  
  
  
  # 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)
  
  
}