a_line_search: Optimize using line search
In PopED: Population (and Individual) Optimal Experimental Design

Description Usage Arguments Value See Also Examples

The function performs a grid search sequentially along design variables. The grid is defined by ls_step_size.

a_line_search(
  poped.db,
  out_file = "",
  bED = FALSE,
  diff = 0,
  fmf_initial = 0,
  dmf_initial = 0,
  opt_xt = poped.db$settings$optsw[2],
  opt_a = poped.db$settings$optsw[4],
  opt_x = poped.db$settings$optsw[3],
  opt_samps = poped.db$settings$optsw[1],
  opt_inds = poped.db$settings$optsw[5],
  ls_step_size = poped.db$settings$ls_step_size
)

`poped.db`	A PopED database.
`out_file`	The output file to write to.
`bED`	If the algorithm should use E-family methods. Logical.
`diff`	The OFV difference that is deemed significant for changing a design. If, by changing a design variable the difference between the new and old OFV is less than `diff` the change is not made.
`fmf_initial`	The initial value of the FIM. If `0` then the FIM is calculated from poped.db.
`dmf_initial`	The initial value of the objective function value (OFV). If `0` then the OFV is calculated from poped.db.
`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_samps`	Are the number of sample times per group being optimized?
`opt_inds`	Are the number of individuals per group being optimized?
`ls_step_size`	Number of grid points in the line search.

A list containing:

`fmf`	The FIM.
`dmf`	The final value of the objective function value.
`best_changed`	If the algorithm has found a better design than the starting design.
`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.
`poped.db`	A PopED database.

Other Optimize: Doptim(), LEDoptim(), RS_opt(), bfgsb_min(), calc_autofocus(), 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)
#####################################


# very sparse grid to evaluate (4 points for each design valiable)
output <- a_line_search(poped.db, opt_xt=TRUE, opt_a=TRUE, ls_step_size=4)

## Not run:   
  
  # longer run time
  output <- a_line_search(poped.db,opt_xt=TRUE)
  
  # output to a text file
  output <- a_line_search(poped.db,opt_xt=TRUE,out_file="tmp.txt")
  

## End(Not run)