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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
#> function (model_switch, xt, parameters, poped.db) 
#> {
#>     with(as.list(parameters), {
#>         y = xt
#>         y = (DOSE * Favail * KA/(V * (KA - CL/V))) * (exp(-CL/V * 
#>             xt) - exp(-KA * xt))
#>         return(list(y = y, poped.db = poped.db))
#>     })
#> }
#> <bytecode: 0x564e4be77e58>
#> <environment: namespace:PopED>

## -- 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"]
#> $ret
#>             [,1]      [,2]      [,3]         [,4]         [,5]        [,6]
#> [1,] 17141.83891 20.838375 10.011000 0.000000e+00     0.000000  0.00000000
#> [2,]    20.83837 17.268051 -3.423641 0.000000e+00     0.000000  0.00000000
#> [3,]    10.01100 -3.423641 49.864697 0.000000e+00     0.000000  0.00000000
#> [4,]     0.00000  0.000000  0.000000 2.324341e+03     9.770352  0.03523364
#> [5,]     0.00000  0.000000  0.000000 9.770352e+00 19083.877564 11.72131703
#> [6,]     0.00000  0.000000  0.000000 3.523364e-02    11.721317 38.85137516
#> [7,]     0.00000  0.000000  0.000000 7.268410e+02  9656.158553 64.78095548
#> [8,]     0.00000  0.000000  0.000000 9.062739e+01   266.487127  2.94728469
#>              [,7]        [,8]
#> [1,]      0.00000    0.000000
#> [2,]      0.00000    0.000000
#> [3,]      0.00000    0.000000
#> [4,]    726.84097   90.627386
#> [5,]   9656.15855  266.487127
#> [6,]     64.78096    2.947285
#> [7,] 192840.20092 6659.569867
#> [8,]   6659.56987  475.500111
#>