Generate a virtual population
httkpop_generate(
method = "direct resampling",
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
)A data.table where each row represents an individual, and each column represents a demographic, anthropometric, or physiological parameter.
The population-generation method to use. Either "virtual individuals" or "direct resampling." Short names may be used: "d" or "dr" for "direct resampling", and "v" or "vi" for "virtual individuals".
The desired number of individuals in the virtual population.
nsamp need not be provided if gendernum is provided.
Optional: A named list giving the numbers of male and
female individuals to include in the population, e.g. list(Male=100,
Female=100). Default is NULL, meaning both males and females are included,
in their proportions in the NHANES data. If both nsamp and
gendernum are provided, they must agree (i.e., nsamp must be
the sum of gendernum).
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 only a single value is provided, both minimum and maximum ages
will be set to that value; e.g. agelim_years=3 is equivalent to
agelim_years=c(3,3). If agelim_years is provided and
agelim_months is not, agelim_years will override the default
value of 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 agelim_years. If only a
single value is provided, both minimum and maximum ages will be set to that
value; e.g. agelim_months=36 is equivalent to
agelim_months=c(36,36). If agelim_months is provided and
agelim_years is not, agelim_months will override the default
values of agelim_years.
Optional: The weight categories to include in the
population. Default is c('Underweight', 'Normal', 'Overweight',
'Obese'). User-supplied vector must contain one or more of these strings.
The kidney function categories to include in the
population. Default is c('Normal','Kidney Disease', 'Kidney Failure')
to include all kidney function levels.
Optional: a character vector giving the races/ethnicities to
include in the population. Default is c('Mexican American','Other
Hispanic','Non-Hispanic White','Non-Hispanic Black','Other'), to include
all races and ethnicities in their proportions in the NHANES data.
User-supplied vector must contain one or more of these strings.
TRUE to add residual variability to GFR predicted from serum creatinine; FALSE to not add residual variability
TRUE to use the CKD-EPI equation as originally published (with a coefficient changing predicted GFR for individuals identified as "Non-Hispanic Black"); FALSE to set this coefficient to 1.
Caroline Ring
Ring, Caroline L., et al. "Identifying populations sensitive to environmental chemicals by simulating toxicokinetic variability." Environment International 106 (2017): 105-118
# \donttest{
#Simply generate a virtual population of 100 individuals,
#using the direct-resampling method
set.seed(42)
httkpop_generate(method='direct resampling', nsamp=100)
#Generate a population using the virtual-individuals method,
#including 80 females and 20 males,
#including only ages 20-65,
#including only Mexican American and
#Non-Hispanic Black individuals,
#including only non-obese individuals
httkpop_generate(method = 'virtual individuals',
gendernum=list(Female=80,
Male=20),
agelim_years=c(20,65),
reths=c('Mexican American',
'Non-Hispanic Black'),
weight_category=c('Underweight',
'Normal',
'Overweight'))
# }
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