feps.add.prop {PopED}

RUV model: Additive and Proportional.

Description

This is a residual unexplained variability (RUV) model function that encodes the model described above. The function is suitable for input to the create.poped.database function using the fError_file argument.

Usage

feps.add.prop(model_switch, xt, parameters, epsi, poped.db)

Arguments

model_switch

a vector of values, the same size as xt, identifying which model response should be computed for the corresponding xt value. Used for multiple response models.

xt

a vector of independent variable values (often time).

parameters

A named list of parameter values.

epsi

A matrix with the same number of rows as the xt vector, columns match the numbers defined in this function.

poped.db

a poped database. This can be used to extract information that may be needed in the model file.

Value

A list consisting of:

1. y the values of the model at the specified points.

2. poped.db A (potentially modified) poped database.

See Also

Other models: feps.add(), feps.prop(), ff.PK.1.comp.oral.md.CL(), ff.PK.1.comp.oral.md.KE(), ff.PK.1.comp.oral.sd.CL(), ff.PK.1.comp.oral.sd.KE(), ff.PKPD.1.comp.oral.md.CL.imax(), ff.PKPD.1.comp.sd.CL.emax()

Other RUV_models: feps.add(), feps.prop()

Examples

library(PopED)

## find the parameters that are needed to define in the structural model
ff.PK.1.comp.oral.md.CL

## -- parameter definition function 
## -- names match parameters in function ff
sfg <- function(x,a,bpop,b,bocc){
  parameters=c( V=bpop[1]*exp(b[1]),
                KA=bpop[2]*exp(b[2]),
                CL=bpop[3]*exp(b[3]),
                Favail=bpop[4],
                DOSE=a[1],
                TAU=a[2])
  return( parameters ) 
}

## -- Define design and design space
poped.db <- create.poped.database(ff_fun=ff.PK.1.comp.oral.md.CL,
                                  fg_fun=sfg,
                                  fError_fun=feps.add.prop,
                                  groupsize=20,
                                  m=2,
                                  sigma=c(0.04,5e-6),
                                  bpop=c(V=72.8,KA=0.25,CL=3.75,Favail=0.9), 
                                  d=c(V=0.09,KA=0.09,CL=0.25^2), 
                                  notfixed_bpop=c(1,1,1,0),
                                  notfixed_sigma=c(0,0),
                                  xt=c( 1,2,8,240,245),
                                  minxt=c(0,0,0,240,240),
                                  maxxt=c(10,10,10,248,248),
                                  a=cbind(c(20,40),c(24,24)),
                                  bUseGrouped_xt=1,
                                  maxa=c(200,24),
                                  mina=c(0,24))

##  create plot of model without variability 
plot_model_prediction(poped.db)

## evaluate initial design
FIM <- evaluate.fim(poped.db) 
FIM
det(FIM)
get_rse(FIM,poped.db)