mftot: Evaluate the Fisher Information Matrix (FIM)

In PopED: Population (and Individual) Optimal Experimental Design

Description

Compute the FIM given specific model(s), parameters, design and methods.

Usage

mftot(
  model_switch,
  groupsize,
  ni,
  xt,
  x,
  a,
  bpop,
  d,
  sigma,
  docc,
  poped.db,
  ...
)

Arguments

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.
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.
bpop	The fixed effects parameter values. Supplied as a vector.
d	A between subject variability matrix (OMEGA in NONMEM).
sigma	A residual unexplained variability matrix (SIGMA in NONMEM).
docc	A between occasion variability matrix.
poped.db	A PopED database.

Value

As a list:

ret	The FIM
poped.db	A PopED database

See Also

For an easier function to use, please see evaluate.fim.

Other FIM: LinMatrixH(), LinMatrixLH(), LinMatrixL_occ(), calc_ofv_and_fim(), ed_laplace_ofv(), ed_mftot(), efficiency(), evaluate.e.ofv.fim(), evaluate.fim(), gradf_eps(), mf3(), mf7(), ofv_criterion(), 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)
#####################################


mftot(model_switch=poped.db$design$model_switch,
      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,
      bpop=poped.db$parameters$param.pt.val$bpop,
      d=poped.db$parameters$param.pt.val$d,
      sigma=poped.db$parameters$sigma,
      docc=poped.db$parameters$param.pt.val$docc,
      poped.db)["ret"]

PopED documentation built on May 21, 2021, 5:08 p.m.