pargen {PopED}

Parameter simulation

Description

Function generates random samples for a list of parameters

Usage

pargen(par, user_dist_pointer, sample_size, bLHS, sample_number, poped.db)

Arguments

par

A matrix describing the parameters. Each row is a parameter and the matrix has three columns:

1. First column - Type of distribution (0-fixed, 1-normal, 2-uniform, 3-user specified, 4-lognormal, 5-Truncated normal).

2. Second column - Mean of distribution.

3. Third column - Variance or range of distribution.

user_dist_pointer

A text string of the name of a function that generates random samples from a user defined distribution.

sample_size

The number of random samples per parameter to generate

bLHS

Logical, indicating if Latin Hypercube Sampling should be used.

sample_number

The sample number to extract from a user distribution.

poped.db

A PopED database.

Value

A matrix of random samples of size (sample_size x number_of_parameters)

Examples

library(PopED)

############# START #################
## Create PopED database
## (warfarin example)
#####################################

## 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. 

## 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 model, parameters, initial design
poped.db <- create.poped.database(ff_fun=ff.PK.1.comp.oral.sd.CL,
                                  fg_fun=sfg,
                                  fError_fun=feps.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),
                                  groupsize=32,
                                  xt=c( 0.5,1,2,6,24,36,72,120),
                                  a=c(DOSE=70))

############# END ###################
## Create PopED database
## (warfarin example)
#####################################


# Adding 40% Uncertainty to fixed effects log-normal (not Favail)
bpop_vals <- c(CL=0.15, V=8, KA=1.0, Favail=1)
bpop_vals_ed_ln <- cbind(ones(length(bpop_vals),1)*4, # log-normal distribution
                      bpop_vals,
                      ones(length(bpop_vals),1)*(bpop_vals*0.4)^2) # 40% of bpop value
bpop_vals_ed_ln["Favail",]  <- c(0,1,0)

pars.ln <- pargen(par=bpop_vals_ed_ln,
               user_dist_pointer=NULL,
               sample_size=1000,
               bLHS=1,
               sample_number=NULL,
               poped.db)


# Adding 10% Uncertainty to fixed effects normal-distribution (not Favail)
bpop_vals_ed_n <- cbind(ones(length(bpop_vals),1)*1, # log-normal distribution
                      bpop_vals,
                      ones(length(bpop_vals),1)*(bpop_vals*0.1)^2) # 10% of bpop value
bpop_vals_ed_n["Favail",]  <- c(0,1,0)

pars.n <- pargen(par=bpop_vals_ed_n,
               user_dist_pointer=NULL,
               sample_size=1000,
               bLHS=1,
               sample_number=NULL,
               poped.db)


# Adding 10% Uncertainty to fixed effects uniform-distribution (not Favail)
bpop_vals_ed_u <- cbind(ones(length(bpop_vals),1)*2, # uniform distribution
                        bpop_vals,
                        ones(length(bpop_vals),1)*(bpop_vals*0.1)) # 10% of bpop value
bpop_vals_ed_u["Favail",]  <- c(0,1,0)

pars.u <- pargen(par=bpop_vals_ed_u,
                 user_dist_pointer=NULL,
                 sample_size=1000,
                 bLHS=1,
                 sample_number=NULL,
                 poped.db)


# Adding user defined distributions
bpop_vals_ed_ud <- cbind(ones(length(bpop_vals),1)*3, # user dfined distribution
                         bpop_vals,
                         bpop_vals*0.1) # 10% of bpop value
bpop_vals_ed_ud["Favail",]  <- c(0,1,0)

# A normal distribution
my_dist <- function(...){
  par_vec <- rnorm(c(1,1,1,1),mean=bpop_vals_ed_ud[,2],sd=bpop_vals_ed_ud[,3])
}

pars.ud <- pargen(par=bpop_vals_ed_ud,
                  user_dist_pointer=my_dist,
                  sample_size=1000,
                  bLHS=1,
                  sample_number=NULL,
                  poped.db)