calc_autofocus: Compute the autofocus portion of the stochastic gradient...
In PopED: Population (and Individual) Optimal Experimental Design

Description Usage Arguments Value See Also Examples

Compute the autofocus portion of the stochastic gradient routine

calc_autofocus(
  m,
  ni_var,
  dmf,
  varopt,
  varopto,
  maxvar,
  minvar,
  gradvar,
  normgvar,
  avar,
  model_switch,
  groupsize,
  xtopt,
  xopt,
  aopt,
  ni,
  bpop,
  d,
  sigma,
  docc,
  poped.db
)

`m`	Number of groups in the study. Each individual in a group will have the same design.
`ni_var`	The ni_var.
`dmf`	The initial OFV. If set to zero then it is computed.
`varopt`	The varopt.
`varopto`	The varopto.
`maxvar`	The maxvar.
`minvar`	The minvar.
`gradvar`	The gradvar.
`normgvar`	The normgvar.
`avar`	The avar.
`model_switch`	A matrix that is the same size as xt, specifying which model each sample belongs to.
`groupsize`	A vector of the number of individuals in each group.
`xtopt`	The optimal sampling times matrix.
`xopt`	The optimal discrete design variables matrix.
`aopt`	The optimal continuous design variables matrix.
`ni`	A vector of the number of samples in each group.
`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 will be worked out automatically.
`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 will be worked out automatically.
`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()`.
`docc`	Matrix defining the IOV, the IOV variances and the IOV distribution as for d and bpop.
`poped.db`	A PopED database.

A list containing:

`navar`	The autofocus parameter.
`poped.db`	PopED database.

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

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)
#####################################


## Not run: 
  
# Stochastic gradient search, DOSE and sample time optimization
sg.output <- poped_optimize(poped.db,opt_xt=1,opt_a=1, 
                            bUseRandomSearch= 0,
                            bUseStochasticGradient = 1,
                            bUseBFGSMinimizer = 0,
                            bUseLineSearch = 0,
                            sgit=20)


## End(Not run)