blockfinal: Result function for optimization routines
In andrewhooker/PopED: Population (and Individual) Optimal Experimental Design

blockfinal

R Documentation

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 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.
`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`.

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))

andrewhooker/PopED documentation built on Nov. 23, 2023, 1:37 a.m.