Description
Usage
Value
Arguments
Author
Details
References
See Also
Examples
Run this code# \donttest{
# Set the number of samples (NSAMP) low for rapid testing, increase NSAMP
# for more stable results. Default value is 1000:
NSAMP = 10
# Basic in vitro - in vivo extrapolation with httk, convert 3 uM in vitro
# concentration of chemical with CAS 2451-62-9 to mg/kg/day:
set.seed(1234)
3/calc_mc_css(chem.cas="2451-62-9", samples=NSAMP, output.units="uM")
# The significant digits should give the same answer as:
set.seed(1234)
calc_mc_oral_equiv(chem.cas="2451-62-9", conc=3, samples=NSAMP)
set.seed(1234)
calc_mc_css(chem.name='Bisphenol A', output.units='uM',
samples=NSAMP, return.samples=TRUE)
set.seed(1234)
calc_mc_css(chem.name='Bisphenol A', output.units='uM',
samples=NSAMP,
httkpop.generate.arg.list=list(method='vi'))
# The following example should result in an error since we do not
# estimate tissue partitioning with '3compartmentss'.
set.seed(1234)
try(calc_mc_css(chem.name='2,4-d', which.quantile=.9,
samples=NSAMP,
httkpop=FALSE, tissue='heart'))
# But heart will work with PBTK, even though it's lumped since we estimate
# a partition coefficient before lumping:
set.seed(1234)
calc_mc_css(chem.name='2,4-d', model='pbtk',
samples=NSAMP,
which.quantile=.9, httkpop=FALSE, tissue='heart')
set.seed(1234)
calc_mc_css(chem.cas = "80-05-7", which.quantile = 0.5,
output.units = "uM", samples = NSAMP,
httkpop.generate.arg.list=list(method='vi', gendernum=NULL,
agelim_years=NULL, agelim_months=NULL,
weight_category = c("Underweight","Normal","Overweight","Obese")))
params <- parameterize_pbtk(chem.cas="80-05-7")
set.seed(1234)
calc_mc_css(parameters=params,model="pbtk", samples=NSAMP)
set.seed(1234)
# Standard HTTK Monte Carlo
calc_mc_css(chem.cas="90-43-7", model="pbtk", samples=NSAMP)
set.seed(1234)
# HTTK Monte Carlo with no measurement uncertainty (pre v1.10.0):
calc_mc_css(chem.cas="90-43-7",
model="pbtk",
samples=NSAMP,
invitro.mc.arg.list = list(
adjusted.Funbound.plasma = TRUE,
poormetab = TRUE,
fup.censored.dist = FALSE,
fup.lod = 0.01,
fup.meas.cv = 0.0,
clint.meas.cv = 0.0,
fup.pop.cv = 0.3,
clint.pop.cv = 0.3))
# HTTK Monte Carlo with no HTTK-Pop physiological variability):
set.seed(1234)
calc_mc_css(chem.cas="90-43-7",model="pbtk",samples=NSAMP,httkpop=FALSE)
# HTTK Monte Carlo with no in vitro uncertainty and variability):
set.seed(1234)
calc_mc_css(chem.cas="90-43-7",model="pbtk",samples=NSAMP,invitrouv=FALSE)
# HTTK Monte Carlo with no HTTK-Pop and no in vitro uncertainty and variability):
set.seed(1234)
calc_mc_css(chem.cas="90-43-7" ,model="pbtk",
samples=NSAMP, httkpop=FALSE, invitrouv=FALSE)
# Should be the same as the mean result:
calc_analytic_css(chem.cas="90-43-7",model="pbtk",output.units="mg/L")
# HTTK Monte Carlo using basic Monte Carlo sampler:
set.seed(1234)
calc_mc_css(chem.cas="90-43-7",
model="pbtk",
samples=NSAMP,
httkpop=FALSE,
invitrouv=FALSE,
vary.params=list(Pow=0.3))
# We can also use the Monte Carlo functions by passing a table
# where each row represents a different Monte Carlo draw of parameters:
p <- create_mc_samples(chem.cas="80-05-7")
# Use data.table for steady-state plasma concentration (Css) Monte Carlo:
calc_mc_css(parameters=p)
# Using the same table gives the same answer:
calc_mc_css(parameters=p)
# Use Css for 1 mg/kg/day for simple reverse toxicokinetics
# in Vitro-In Vivo Extrapolation to convert 15 uM to mg/kg/day:
15/calc_mc_css(parameters=p, output.units="uM")
# Can do the same with calc_mc_oral_equiv:
calc_mc_oral_equiv(15, parameters=p)
# }
Run the code above in your browser using DataLab