PopED (version 0.7.0)

blockfinal: Result function for optimization routines

Description

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 file or "" will output to the screen.

trflag

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

footer_flag

Should the footer text be printed out?

...

arguments passed to evaluate.fim and ofv_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

## -- 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))