httkpop_virtual_indiv {httk} | R Documentation |
Generate a virtual population by the virtual individuals method.
Description
Generate a virtual population by the virtual individuals method.
Usage
httkpop_virtual_indiv(
nsamp = NULL,
gendernum = NULL,
agelim_years = NULL,
agelim_months = NULL,
weight_category = c("Underweight", "Normal", "Overweight", "Obese"),
gfr_category = c("Normal", "Kidney Disease", "Kidney Failure"),
reths = c("Mexican American", "Other Hispanic", "Non-Hispanic White",
"Non-Hispanic Black", "Other"),
gfr_resid_var = TRUE,
ckd_epi_race_coeff = FALSE,
nhanes_mec_svy
)
Arguments
nsamp |
The desired number of individuals in the virtual population.
|
gendernum |
Optional: A named list giving the numbers of male and female
individuals to include in the population, e.g. |
agelim_years |
Optional: A two-element numeric vector giving the minimum
and maximum ages (in years) to include in the population. Default is
c(0,79). If |
agelim_months |
Optional: A two-element numeric vector giving the minimum
and maximum ages (in months) to include in the population. Default is c(0,
959), equivalent to the default |
weight_category |
Optional: The weight categories to include in the
population. Default is |
gfr_category |
The kidney function categories to include in the
population. Default is |
reths |
Optional: a character vector giving the races/ethnicities to
include in the population. Default is |
gfr_resid_var |
Logical value indicating whether or not to include residual variability when generating GFR values. (Default is TRUE.) |
ckd_epi_race_coeff |
Logical value indicating whether or not to use the "race coefficient" from the CKD-EPI equation when estimating GFR values. (Default is FALSE.) |
nhanes_mec_svy |
|
Value
A data.table where each row represents an individual, and each column represents a demographic, anthropometric, or physiological parameter.
Author(s)
Caroline Ring
References
Ring CL, Pearce RG, Setzer RW, Wetmore BA, Wambaugh JF (2017). “Identifying populations sensitive to environmental chemicals by simulating toxicokinetic variability.” Environment International, 106, 105–118.