Compute a normalized OFV based on the size of the FIM matrix. This value can then be used in efficiency calculations. This is NOT the OFV used in optimization, see ofv_fim.

ofv_criterion(
  ofv_f,
  num_parameters,
  poped.db,
  ofv_calc_type = poped.db$settings$ofv_calc_type
)

Arguments

ofv_f

An objective function

num_parameters

The number of parameters to use for normalization

poped.db

a poped database

ofv_calc_type

OFV calculation type for FIM

  • 1 = "D-optimality". Determinant of the FIM: det(FIM)

  • 2 = "A-optimality". Inverse of the sum of the expected parameter variances: 1/trace_matrix(inv(FIM))

  • 4 = "lnD-optimality". Natural logarithm of the determinant of the FIM: log(det(FIM))

  • 6 = "Ds-optimality". Ratio of the Determinant of the FIM and the Determinant of the uninteresting rows and columns of the FIM: det(FIM)/det(FIM_u)

  • 7 = Inverse of the sum of the expected parameter RSE: 1/sum(get_rse(FIM,poped.db,use_percent=FALSE))

Value

The specified criterion value.

See also

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: 0x7fe20a979808> #> <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) ##################################### ## evaluate initial design FIM <- evaluate.fim(poped.db) # new name for function needed FIM
#> [,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
get_rse(FIM,poped.db)
#> CL V KA d_CL d_V d_KA sig_prop sig_add #> 5.096246 3.031164 14.260384 29.761226 36.681388 26.748640 32.011719 25.637971
ofv_criterion(ofv_fim(FIM,poped.db,ofv_calc_type=1), length(get_unfixed_params(poped.db)[["all"]]), poped.db, ofv_calc_type=1) # det(FIM)
#> [1] 1016.943
ofv_criterion(ofv_fim(FIM,poped.db,ofv_calc_type=2), length(get_unfixed_params(poped.db)[["all"]]), poped.db, ofv_calc_type=2)
#> [1] 1.140916
ofv_criterion(ofv_fim(FIM,poped.db,ofv_calc_type=4), length(get_unfixed_params(poped.db)[["all"]]), poped.db, ofv_calc_type=4)
#> [1] 1016.943
ofv_criterion(ofv_fim(FIM,poped.db,ofv_calc_type=6), length(get_unfixed_params(poped.db)[["all"]]), poped.db, ofv_calc_type=6)
#> [1] 1.75168
ofv_criterion(ofv_fim(FIM,poped.db,ofv_calc_type=7), length(get_unfixed_params(poped.db)[["all"]]), poped.db, ofv_calc_type=7)
#> [1] 0