Package 'GeoTox' reference manual

Title:	Spatiotemporal Mixture Risk Assessment
Description:	Connecting spatiotemporal exposure to individual and population-level risk via source-to-outcome continuum modeling. The package, methods, and case-studies are described in Messier, Reif, and Marvel (2024) <doi:10.1101/2024.09.23.24314096> and Eccles et al. (2023) <doi:10.1016/j.scitotenv.2022.158905>.
Authors:	Skylar Marvel [aut] , David Reif [aut] , Kyle Messier [cre, aut] , Spatiotemporal Exposures and Toxicology Group [cph]
Maintainer:	Kyle Messier <[email protected]>
License:	MIT + file LICENSE
Version:	0.2.0
Built:	2024-11-16 13:09:17 UTC
Source:	CRAN

Calculate the mixture response from one of three different approaches: IA, GCA, or Hazard Quotient

Description

Calculate the combined response of multiple chemicals. It calculates the generalized concentration addition response, the independent action response, and a hazard quotient

Usage

calc_concentration_response(
  C_invitro,
  hill_params,
  max_mult = 1.5,
  fixed = FALSE
)
calc_concentration_response(
  C_invitro,
  hill_params,
  max_mult = 1.5,
  fixed = FALSE
)

Arguments

`C_invitro`	in vitro concentrations
`hill_params`	output from `fit_hill()`
`max_mult`	upper bound multiplier for max response
`fixed`	if TRUE, sd = 0

Value

list of data frames

Examples

C_invitro <- list(
  matrix(1:8 / 1e3, ncol = 2, dimnames = list(NULL, c("c1", "c2"))),
  matrix(9:16 / 1e3, ncol = 2, dimnames = list(NULL, c("c1", "c2")))
)
hill_params <- fit_hill(
  data.frame(chem = rep(c("c1", "c2"), each = 3),
             logc = c(-1, 0, 1, 0, 1, 2),
             resp = c(10, 5, 0, 4, 2, 0) / 10),
  chem = "chem"
)

calc_concentration_response(C_invitro, hill_params)
calc_concentration_response(C_invitro, hill_params, fixed = TRUE)
C_invitro <- list(
  matrix(1:8 / 1e3, ncol = 2, dimnames = list(NULL, c("c1", "c2"))),
  matrix(9:16 / 1e3, ncol = 2, dimnames = list(NULL, c("c1", "c2")))
)
hill_params <- fit_hill(
  data.frame(chem = rep(c("c1", "c2"), each = 3),
             logc = c(-1, 0, 1, 0, 1, 2),
             resp = c(10, 5, 0, 4, 2, 0) / 10),
  chem = "chem"
)

calc_concentration_response(C_invitro, hill_params)
calc_concentration_response(C_invitro, hill_params, fixed = TRUE)

Independent Action

Description

Calculate independent action response for a set of chemicals with Hill concentration-response curves.

Usage

calc_independent_action(conc, max, AC50, Emax, n = 1)
calc_independent_action(conc, max, AC50, Emax, n = 1)

Arguments

`conc`	concentrations in regular space
`max`	maximal (asymptotic) responses
`AC50`	concentrations of half-maximal response
`Emax`	maximum mixture response
`n`	Hill coefficients (slopes)

Details

The concentration is computed as:

$IA = E_{max} \times \left( 1 - \prod\limits_{i} \left(1 - \frac{x_i}{E_{max}}\right) \right),$

where $x_i = hill\_val(conc_i, max_i, AC50_i, n_i)$ is the Hill model response function for each chemical.

Value

response value

Examples

n_chem <- 5
conc <- 10^sample(-1:4, n_chem, replace = TRUE)
max <- 80 * runif(n_chem)
AC50 <- 10^(5 * runif(n_chem) - 1)
Emax <- 100

calc_independent_action(conc, max, AC50, Emax)
n_chem <- 5
conc <- 10^sample(-1:4, n_chem, replace = TRUE)
max <- 80 * runif(n_chem)
AC50 <- 10^(5 * runif(n_chem) - 1)
Emax <- 100

calc_independent_action(conc, max, AC50, Emax)

Calculate internal chemical dose

Description

Estimate the internal dose from inhalation of a chemical given inhalation rate, time, and body weight

Usage

calc_internal_dose(C_ext, IR, time = 1, BW = 1, scaling = 1)
calc_internal_dose(C_ext, IR, time = 1, BW = 1, scaling = 1)

Arguments

`C_ext`	ambient chemical concentration in $\frac{mg}{m^3}$
`IR`	inhalation rate in $\frac{m^3}{day}$
`time`	total time in $days$
`BW`	body weight in $kg$
`scaling`	scaling factor encompassing any required unit adjustments

Details

Input C_ext must be a matrix or list of matrices. Input IR must be an atomic vector or list of atomic vectors. The time, BW and scaling arguments are scalars.

The internal dose is calculated as:

$D_{int} = \frac{C_{ext} \times IR \times time}{BW} \times scaling$

Value

list of matrices containing internal chemical doses in $\frac{mg}{kg}$

Examples

# Single population
C_ext <- matrix(1:15, ncol = 3)
IR <- 1:5
calc_internal_dose(C_ext, IR)

# Multiple populations
C_ext <- list(
  "a" = matrix(1:15 / 10, ncol = 3),
  "b" = matrix(1:8, ncol = 2)
)
IR <- list(1:5, 1:4 / 2)
calc_internal_dose(C_ext, IR)

# Single population
C_ext <- matrix(1:15, ncol = 3)
IR <- 1:5
calc_internal_dose(C_ext, IR)

# Multiple populations
C_ext <- list(
  "a" = matrix(1:15 / 10, ncol = 3),
  "b" = matrix(1:8, ncol = 2)
)
IR <- list(1:5, 1:4 / 2)
calc_internal_dose(C_ext, IR)

Calculate in vitro concentration

Description

Estimate the in vitro equivalent plasma concentration given internal chemical dose and steady-state plasma concentration.

Usage

calc_invitro_concentration(D_int, C_ss = NULL)
calc_invitro_concentration(D_int, C_ss = NULL)

Arguments

`D_int`	internal chemical dose in $\frac{mg}{kg}$
`C_ss`	steady-state plasma concentration in $\frac{\mu M}{mg / kg}$

Details

Input D_int must be a matrix or list of matrices. Input C_ss must be a numeric atomic vector or matrix, or a list of those types.

The in vitro equivalent plasma concentration is calculated as:

$C_{plasma} = C_{ss} \times D_{int}$

Value

list of matrices containing concentrations in $\mu M$

Examples

# Single population
D_int <- matrix(1:15, ncol = 3)
C_ss <- 1:5
calc_invitro_concentration(D_int, C_ss)

# Multiple populations
D_int <- list(
  "a" = matrix(1:15 / 10, ncol = 3),
  "b" = matrix(1:8, ncol = 2)
)
C_ss <- list(1:5, 1:4 / 2)
calc_invitro_concentration(D_int, C_ss)

# Single population
D_int <- matrix(1:15, ncol = 3)
C_ss <- 1:5
calc_invitro_concentration(D_int, C_ss)

# Multiple populations
D_int <- list(
  "a" = matrix(1:15 / 10, ncol = 3),
  "b" = matrix(1:8, ncol = 2)
)
C_ss <- list(1:5, 1:4 / 2)
calc_invitro_concentration(D_int, C_ss)

Calculate response

Description

Calculate mixture response for GeoTox population data

Usage

calculate_response(x, ...)
calculate_response(x, ...)

Arguments

`x`	GeoTox object
`...`	additional arguments passed to other functions. See details.

Details

Additional parameters include time, BW, and scaling for calc_internal_dose, and max_mult for calc_concentration_response.

Value

The same object with additional fields added or updated

Examples

# Use a subset of the package data for demonstration purposes
set.seed(2357)
n <- 10 # Population size
m <- 5 # Number of regions
idx <- if (m < 100) sample(1:100, m) else 1:100

# Create GeoTox object and populate required fields
geoTox <- GeoTox() |>
  # Simulate populations for each region
  simulate_population(age = split(geo_tox_data$age, ~FIPS)[idx],
                      obesity = geo_tox_data$obesity[idx, ],
                      exposure = split(geo_tox_data$exposure, ~FIPS)[idx],
                      simulated_css = geo_tox_data$simulated_css,
                      n = n) |>
  # Estimated Hill parameters
  set_hill_params(geo_tox_data$dose_response |>
                    fit_hill(assay = "endp", chem = "casn") |>
                    dplyr::filter(!tp.sd.imputed, !logAC50.sd.imputed))

# Response computations can now be done
geoTox <- geoTox |> calculate_response()
# Use a subset of the package data for demonstration purposes
set.seed(2357)
n <- 10 # Population size
m <- 5 # Number of regions
idx <- if (m < 100) sample(1:100, m) else 1:100

# Create GeoTox object and populate required fields
geoTox <- GeoTox() |>
  # Simulate populations for each region
  simulate_population(age = split(geo_tox_data$age, ~FIPS)[idx],
                      obesity = geo_tox_data$obesity[idx, ],
                      exposure = split(geo_tox_data$exposure, ~FIPS)[idx],
                      simulated_css = geo_tox_data$simulated_css,
                      n = n) |>
  # Estimated Hill parameters
  set_hill_params(geo_tox_data$dose_response |>
                    fit_hill(assay = "endp", chem = "casn") |>
                    dplyr::filter(!tp.sd.imputed, !logAC50.sd.imputed))

# Response computations can now be done
geoTox <- geoTox |> calculate_response()

Compute response sensitivity to parameter variation.

Description

Compute response sensitivity to parameter variation.

Usage

compute_sensitivity(
  x,
  vary = c("age", "obesity", "css_params", "fit_params", "C_ext"),
  max_mult = NULL
)
compute_sensitivity(
  x,
  vary = c("age", "obesity", "css_params", "fit_params", "C_ext"),
  max_mult = NULL
)

Arguments

`x`	GeoTox object.
`vary`	which parameter to vary.
`max_mult`	input for calc_concentration_response step.

Value

output from calc_concentration_response

Examples

# Use a subset of the package data for demonstration purposes
set.seed(2357)
n <- 10 # Population size
m <- 5 # Number of regions
idx <- if (m < 100) sample(1:100, m) else 1:100

# Create GeoTox object and populate required fields
geoTox <- GeoTox() |>
  # Simulate populations for each region
  simulate_population(age = split(geo_tox_data$age, ~FIPS)[idx],
                      obesity = geo_tox_data$obesity[idx, ],
                      exposure = split(geo_tox_data$exposure, ~FIPS)[idx],
                      simulated_css = geo_tox_data$simulated_css,
                      n = n) |>
  # Estimated Hill parameters
  set_hill_params(geo_tox_data$dose_response |>
                    fit_hill(assay = "endp", chem = "casn") |>
                    dplyr::filter(!tp.sd.imputed, !logAC50.sd.imputed))

# Sensitivity computations can now be done
age_resp <- geoTox |> compute_sensitivity()
obesity_resp <- geoTox |> compute_sensitivity(vary = "obesity")
# Use a subset of the package data for demonstration purposes
set.seed(2357)
n <- 10 # Population size
m <- 5 # Number of regions
idx <- if (m < 100) sample(1:100, m) else 1:100

# Create GeoTox object and populate required fields
geoTox <- GeoTox() |>
  # Simulate populations for each region
  simulate_population(age = split(geo_tox_data$age, ~FIPS)[idx],
                      obesity = geo_tox_data$obesity[idx, ],
                      exposure = split(geo_tox_data$exposure, ~FIPS)[idx],
                      simulated_css = geo_tox_data$simulated_css,
                      n = n) |>
  # Estimated Hill parameters
  set_hill_params(geo_tox_data$dose_response |>
                    fit_hill(assay = "endp", chem = "casn") |>
                    dplyr::filter(!tp.sd.imputed, !logAC50.sd.imputed))

# Sensitivity computations can now be done
age_resp <- geoTox |> compute_sensitivity()
obesity_resp <- geoTox |> compute_sensitivity(vary = "obesity")

Fit 2- or 3-parameter Hill model

Description

Fit 2- or 3-parameter Hill model

Usage

fit_hill(
  x,
  conc = "logc",
  resp = "resp",
  fixed_slope = TRUE,
  chem = NULL,
  assay = NULL
)
fit_hill(
  x,
  conc = "logc",
  resp = "resp",
  fixed_slope = TRUE,
  chem = NULL,
  assay = NULL
)

Arguments

`x`	data frame of dose response data.
`conc`	column name of base-10 log scaled concentration.
`resp`	column name of response.
`fixed_slope`	if TRUE, slope is fixed at 1.
`chem`	(optional) column name of chemical identifiers.
`assay`	(optional) column name of assay identifiers.

Details

Optional chem and assay identifiers can be used to fit multiple chemicals and/or assays. Returned columns tp is the top asymptote and logAC50 is the 50% response concentration. If the computation of the standard deviations of these two parameters fails, then the standard deviation is set equal to the parameter estimate and is indicated by the respective imputed flag being TRUE.

Value

data frame of fit parameters.

Examples

# Multiple assays, multiple chemicals
df <- geo_tox_data$dose_response
fit_hill(df, assay = "endp", chem = "casn")

# Single assay, multiple chemicals
df <- geo_tox_data$dose_response |>
  dplyr::filter(endp == "TOX21_H2AX_HTRF_CHO_Agonist_ratio")
fit_hill(df, chem = "casn")

# Single assay, single chemical
df <- geo_tox_data$dose_response |>
  dplyr::filter(endp == "TOX21_H2AX_HTRF_CHO_Agonist_ratio",
                casn == "510-15-6")
fit_hill(df)
# 3-parameter Hill model
fit_hill(df, fixed_slope = FALSE)
# Multiple assays, multiple chemicals
df <- geo_tox_data$dose_response
fit_hill(df, assay = "endp", chem = "casn")

# Single assay, multiple chemicals
df <- geo_tox_data$dose_response |>
  dplyr::filter(endp == "TOX21_H2AX_HTRF_CHO_Agonist_ratio")
fit_hill(df, chem = "casn")

# Single assay, single chemical
df <- geo_tox_data$dose_response |>
  dplyr::filter(endp == "TOX21_H2AX_HTRF_CHO_Agonist_ratio",
                casn == "510-15-6")
fit_hill(df)
# 3-parameter Hill model
fit_hill(df, fixed_slope = FALSE)

GeoTox Data

Description

Sample data for use in vignettes and function examples. See the Package Data vignette, vignette("package_data", package = "GeoTox"), for details on how this data was gathered.

Usage

geo_tox_data
geo_tox_data

Format

A list with items:

exposure: 2019 AirToxScreen exposure concentrations for a subset of chemicals in North Carolina counties.
dose_response: Subset of chemicals curated by ICE cHTS as active within a set of assays.
age: County population estimates for 7/1/2019 in North Carolina.
obesity: CDC PLACES obesity data for North Carolina counties in 2020.
simulated_css: Simulated steady-state plasma concentrations for various age groups and obesity status combinations.
boundaries: County and state boundaries for North Carolina in 2019.

GeoTox S3 object

Description

An S3 object that can be used to help organize the data and results of a GeoTox analysis.

Usage

GeoTox()

## S3 method for class 'GeoTox'
plot(x, type = c("resp", "hill", "exposure", "sensitivity"), ...)
GeoTox()

## S3 method for class 'GeoTox'
plot(x, type = c("resp", "hill", "exposure", "sensitivity"), ...)

Arguments

`x`	GeoTox object.
`type`	type of plot.
`...`	arguments passed to subsequent methods.

Value

a GeoTox S3 object

Examples

# Use a subset of the package data for demonstration purposes
set.seed(2357)
n <- 10 # Population size
m <- 5 # Number of regions
idx <- if (m < 100) sample(1:100, m) else 1:100

geoTox <- GeoTox() |> 
  # Set region and group boundaries (for plotting)
  set_boundaries(region = geo_tox_data$boundaries$county,
                 group  = geo_tox_data$boundaries$state) |> 
  # Simulate populations for each region
  simulate_population(age           = split(geo_tox_data$age, ~FIPS)[idx],
                      obesity       = geo_tox_data$obesity[idx, ],
                      exposure      = split(geo_tox_data$exposure, ~FIPS)[idx],
                      simulated_css = geo_tox_data$simulated_css,
                      n             = n) |> 
  # Estimated Hill parameters
  set_hill_params(geo_tox_data$dose_response |>
                    fit_hill(assay = "endp", chem = "casn") |> 
                    dplyr::filter(!tp.sd.imputed, !logAC50.sd.imputed)) |>
  # Calculate response
  calculate_response() |>
  # Perform sensitivity analysis
  sensitivity_analysis()

# Print GeoTox object
geoTox

# Plot hill fits
plot(geoTox, type = "hill")
# Plot exposure data
plot(geoTox, type = "exposure", ncol = 5)
# Plot response data
plot(geoTox)
plot(geoTox, assays = "TOX21_H2AX_HTRF_CHO_Agonist_ratio")
# Plot sensitivity data
plot(geoTox, type = "sensitivity")
plot(geoTox, type = "sensitivity", assay = "TOX21_H2AX_HTRF_CHO_Agonist_ratio")
# Use a subset of the package data for demonstration purposes
set.seed(2357)
n <- 10 # Population size
m <- 5 # Number of regions
idx <- if (m < 100) sample(1:100, m) else 1:100

geoTox <- GeoTox() |> 
  # Set region and group boundaries (for plotting)
  set_boundaries(region = geo_tox_data$boundaries$county,
                 group  = geo_tox_data$boundaries$state) |> 
  # Simulate populations for each region
  simulate_population(age           = split(geo_tox_data$age, ~FIPS)[idx],
                      obesity       = geo_tox_data$obesity[idx, ],
                      exposure      = split(geo_tox_data$exposure, ~FIPS)[idx],
                      simulated_css = geo_tox_data$simulated_css,
                      n             = n) |> 
  # Estimated Hill parameters
  set_hill_params(geo_tox_data$dose_response |>
                    fit_hill(assay = "endp", chem = "casn") |> 
                    dplyr::filter(!tp.sd.imputed, !logAC50.sd.imputed)) |>
  # Calculate response
  calculate_response() |>
  # Perform sensitivity analysis
  sensitivity_analysis()

# Print GeoTox object
geoTox

# Plot hill fits
plot(geoTox, type = "hill")
# Plot exposure data
plot(geoTox, type = "exposure", ncol = 5)
# Plot response data
plot(geoTox)
plot(geoTox, assays = "TOX21_H2AX_HTRF_CHO_Agonist_ratio")
# Plot sensitivity data
plot(geoTox, type = "sensitivity")
plot(geoTox, type = "sensitivity", assay = "TOX21_H2AX_HTRF_CHO_Agonist_ratio")

Get `C_ss` Data for Fixed Age

Description

Get C_ss Data for Fixed Age

Usage

get_fixed_age(simulated_css, age)
get_fixed_age(simulated_css, age)

Arguments

`simulated_css`	list of pre-generated `C_ss` data, for details see: `vignette("package_data", package = "GeoTox")`.
`age`	list of atomic vectors containing ages.

Value

list of matrices containing median C_ss values.

Examples

get_fixed_age(simulated_css = geo_tox_data$simulated_css,
              age = list(c(25, 35, 55), c(15, 60)))
get_fixed_age(simulated_css = geo_tox_data$simulated_css,
              age = list(c(25, 35, 55), c(15, 60)))

Get Fixed `C_ss` Data

Description

Get C_ss values for use in sensitivity_analysis and compute_sensitivity.

Usage

get_fixed_css(simulated_css, age, obesity, C_ss)
get_fixed_css(simulated_css, age, obesity, C_ss)

Arguments

`simulated_css`	list of pre-generated `C_ss` data, for details see: `vignette("package_data", package = "GeoTox")`.
`age`	list of atomic vectors containing ages.
`obesity`	list of atomic vectors containing obesity status.
`C_ss`	list of matrices containing `C_ss` values.

Value

list of matrices or atomic vectors containing C_ss values.

Examples

# Define inputs
age <- list(c(25, 35, 55),
            c(15, 60))
obesity <- list(c("Obese", "Normal", "Obese"),
                c("Normal", "Normal"))
C_ss <- sample_Css(simulated_css = geo_tox_data$simulated_css,
                   age = age,
                   obesity = obesity)

# Get fixed C_ss data
get_fixed_css(simulated_css = geo_tox_data$simulated_css,
              age = age,
              obesity = obesity,
              C_ss = C_ss)
# Define inputs
age <- list(c(25, 35, 55),
            c(15, 60))
obesity <- list(c("Obese", "Normal", "Obese"),
                c("Normal", "Normal"))
C_ss <- sample_Css(simulated_css = geo_tox_data$simulated_css,
                   age = age,
                   obesity = obesity)

# Get fixed C_ss data
get_fixed_css(simulated_css = geo_tox_data$simulated_css,
              age = age,
              obesity = obesity,
              C_ss = C_ss)

Get `C_ss` Data for Fixed Obesity Status

Description

Get C_ss Data for Fixed Obesity Status

Usage

get_fixed_obesity(simulated_css, obesity)
get_fixed_obesity(simulated_css, obesity)

Arguments

`simulated_css`	list of pre-generated `C_ss` data, for details see: `vignette("package_data", package = "GeoTox")`.
`obesity`	list of atomic vectors containing obesity status.

Value

list of matrices containing median C_ss values.

Examples

get_fixed_obesity(simulated_css = geo_tox_data$simulated_css,
                  obesity = list(c("Obese", "Normal", "Obese"),
                                 c("Normal", "Normal")))
get_fixed_obesity(simulated_css = geo_tox_data$simulated_css,
                  obesity = list(c("Obese", "Normal", "Obese"),
                                 c("Normal", "Normal")))

Get median `C_ss` Values

Description

Get median C_ss Values

Usage

get_fixed_other(C_ss)
get_fixed_other(C_ss)

Arguments

C_ss

list of matrices containing C_ss data

Value

list of atomic vectors containing median C_ss values.

Examples

# Generate input C_ss data
age <- list(c(25, 35, 55),
            c(15, 60))
obesity <- list(c("Obese", "Normal", "Obese"),
                c("Normal", "Normal"))
C_ss <- sample_Css(simulated_css = geo_tox_data$simulated_css,
                   age = age,
                   obesity = obesity)

# Get median C_ss values
get_fixed_other(C_ss)
# Generate input C_ss data
age <- list(c(25, 35, 55),
            c(15, 60))
obesity <- list(c("Obese", "Normal", "Obese"),
                c("Normal", "Normal"))
C_ss <- sample_Css(simulated_css = geo_tox_data$simulated_css,
                   age = age,
                   obesity = obesity)

# Get median C_ss values
get_fixed_other(C_ss)

Get `C_ss` Data for Fixed `C_ss` Generation Parameters

Description

Get C_ss Data for Fixed C_ss Generation Parameters

Usage

get_fixed_params(simulated_css, age)
get_fixed_params(simulated_css, age)

Arguments

`simulated_css`	list of pre-generated `C_ss` data, for details see: `vignette("package_data", package = "GeoTox")`.
`age`	list of atomic vectors containing ages.

Value

list of matrices containing C_ss values.

Examples

get_fixed_params(simulated_css = geo_tox_data$simulated_css,
                 age = list(c(25, 35, 55), c(15, 60)))
get_fixed_params(simulated_css = geo_tox_data$simulated_css,
                 age = list(c(25, 35, 55), c(15, 60)))

Hill model concentration

Description

Calculate the concentration in regular space for a given response value.

Usage

hill_conc(resp, max, AC50, n)
hill_conc(resp, max, AC50, n)

Arguments

`resp`	response value
`max`	maximal (asymptotic) response
`AC50`	concentration of half-maximal response
`n`	Hill coefficient (slope)

Details

This is a regular space version of tcpl::tcplHillConc().

The concentration is computed as:

$conc = AC50 * (\frac{max}{resp} - 1)^{-1 / n}$

Value

concentration in regular space

Examples

hill_conc(c(0.2, 0.5, 0.75), 1, 0.01, 1)
hill_conc(c(0.2, 0.5, 0.9), 1, c(0.1, 0.01, 0.001), 2)
hill_conc(c(0.2, 0.5, 0.75), 1, 0.01, 1)
hill_conc(c(0.2, 0.5, 0.9), 1, c(0.1, 0.01, 0.001), 2)

Hill model response

Description

Calculate the response for a given concentration in regular space.

Usage

hill_val(conc, max, AC50, n)
hill_val(conc, max, AC50, n)

Arguments

`conc`	concentration in regular space
`max`	maximal (asymptotic) response
`AC50`	concentration of half-maximal response
`n`	Hill coefficient (slope)

Details

This is a regular space version of tcpl::tcplHillVal().

The Hill model is defined as:

$resp = \frac{max}{1 + (\frac{AC50}{conc})^{n}}$

Value

response value

Examples

hill_val(c(0.0025, 0.01, 0.03), 1, 0.01, 1)
hill_val(c(0.05, 0.01, 0.003), 1, c(0.1, 0.01, 0.001), 2)
hill_val(c(0.0025, 0.01, 0.03), 1, 0.01, 1)
hill_val(c(0.05, 0.01, 0.003), 1, c(0.1, 0.01, 0.001), 2)

Plot exposure data.

Description

Plot exposure data.

Usage

plot_exposure(
  exposure,
  region_boundary,
  group_boundary = NULL,
  chem_label = "chnm",
  ncol = 2
)
plot_exposure(
  exposure,
  region_boundary,
  group_boundary = NULL,
  chem_label = "chnm",
  ncol = 2
)

Arguments

`exposure`	list of exposure data named by region label.
`region_boundary`	"sf" data.frame mapping features to a "geometry" column. Used to color regions.
`group_boundary`	(optional) "sf" data.frame containing a "geometry" column. Used to draw outlines.
`chem_label`	label for facet_wrap.
`ncol`	number of columns to wrap.

Value

ggplot2 object.

Examples

# Load package data
exposure <- split(geo_tox_data$exposure, ~FIPS)
region_boundary <- geo_tox_data$boundaries$county
group_boundary <- geo_tox_data$boundaries$state

# Plot county exposure data
# Use CASN as label to avoid long chemical names
plot_exposure(exposure,
              region_boundary,
              chem_label = "casn",
              ncol = 5)

# Add state boundaries
plot_exposure(exposure,
              region_boundary,
              group_boundary = group_boundary,
              chem_label = "casn",
              ncol = 5)
# Load package data
exposure <- split(geo_tox_data$exposure, ~FIPS)
region_boundary <- geo_tox_data$boundaries$county
group_boundary <- geo_tox_data$boundaries$state

# Plot county exposure data
# Use CASN as label to avoid long chemical names
plot_exposure(exposure,
              region_boundary,
              chem_label = "casn",
              ncol = 5)

# Add state boundaries
plot_exposure(exposure,
              region_boundary,
              group_boundary = group_boundary,
              chem_label = "casn",
              ncol = 5)

Plot Hill equation fits.

Description

Plot Hill equation fits.

Usage

plot_hill(hill_params, xlim = c(-1, 4))
plot_hill(hill_params, xlim = c(-1, 4))

Arguments

`hill_params`	output from `fit_hill`.
`xlim`	log-10 scaled concentration limits.

Value

ggplot2 object.

Examples

# Multiple assays, multiple chemicals
df <- geo_tox_data$dose_response
plot_hill(fit_hill(df, assay = "endp", chem = "casn"))

# Single assay, multiple chemicals
df <- geo_tox_data$dose_response |>
  dplyr::filter(endp == "TOX21_H2AX_HTRF_CHO_Agonist_ratio")
fig <- plot_hill(fit_hill(df, chem = "casn"))
fig
# Modify plot
fig + ggplot2::guides(color = ggplot2::guide_legend(title = "Chemical\nCASN"))

# Single assay, single chemical
df <- geo_tox_data$dose_response |>
  dplyr::filter(endp == "TOX21_H2AX_HTRF_CHO_Agonist_ratio",
                casn == "510-15-6")
plot_hill(fit_hill(df))
# 3-parameter Hill model
plot_hill(fit_hill(df, fixed_slope = FALSE))
# Multiple assays, multiple chemicals
df <- geo_tox_data$dose_response
plot_hill(fit_hill(df, assay = "endp", chem = "casn"))

# Single assay, multiple chemicals
df <- geo_tox_data$dose_response |>
  dplyr::filter(endp == "TOX21_H2AX_HTRF_CHO_Agonist_ratio")
fig <- plot_hill(fit_hill(df, chem = "casn"))
fig
# Modify plot
fig + ggplot2::guides(color = ggplot2::guide_legend(title = "Chemical\nCASN"))

# Single assay, single chemical
df <- geo_tox_data$dose_response |>
  dplyr::filter(endp == "TOX21_H2AX_HTRF_CHO_Agonist_ratio",
                casn == "510-15-6")
plot_hill(fit_hill(df))
# 3-parameter Hill model
plot_hill(fit_hill(df, fixed_slope = FALSE))

Plot response data

Description

Plot response data

Usage

plot_resp(
  df,
  region_boundary,
  group_boundary = NULL,
  assay_quantiles = c(Median = 0.5),
  summary_quantiles = c(`10th percentile` = 0.1)
)
plot_resp(
  df,
  region_boundary,
  group_boundary = NULL,
  assay_quantiles = c(Median = 0.5),
  summary_quantiles = c(`10th percentile` = 0.1)
)

Arguments

`df`	output from resp_quantiles.
`region_boundary`	"sf" data.frame mapping features to a "geometry" column. Used to color map regions.
`group_boundary`	"sf" data.frame containing a "geometry" column. Used to draw outlines around groups of regions.
`assay_quantiles`	named numeric vector of assay quantile labels.
`summary_quantiles`	named numeric vector of summary quantile labels.

Value

ggplot2 object.

Examples

# Use example boundary data from package
region_boundary <- geo_tox_data$boundaries$county
group_boundary <- geo_tox_data$boundaries$state
n <- nrow(region_boundary)

# Single assay quantile
df <- data.frame(id = region_boundary$FIPS,
                 metric = "GCA.Eff",
                 assay_quantile = 0.5,
                 value = runif(n)^3)
# Default plot
plot_resp(df, region_boundary)
# Add group boundary, a state border in this case
plot_resp(df, region_boundary, group_boundary)
# Change quantile label
plot_resp(df, region_boundary, group_boundary,
          assay_quantiles = c("Q50" = 0.5))

# Multiple assay quantiles
df <- data.frame(id = rep(region_boundary$FIPS, 2),
                 metric = "GCA.Eff",
                 assay_quantile = rep(c(0.25, 0.75), each = n),
                 value = c(runif(n)^3, runif(n)^3 + 0.15))
plot_resp(df, region_boundary, group_boundary,
          assay_quantiles = c("Q25" = 0.25, "Q75" = 0.75))

# Summary quantiles
df <- data.frame(id = rep(region_boundary$FIPS, 4),
                 assay_quantile = rep(rep(c(0.25, 0.75), each = n), 2),
                 summary_quantile = rep(c(0.05, 0.95), each = n * 2),
                 metric = "GCA.Eff",
                 value = c(runif(n)^3, runif(n)^3 + 0.15,
                           runif(n)^3 + 0.7, runif(n)^3 + 0.85))
plot_resp(df, region_boundary, group_boundary,
          assay_quantiles = c("A_Q25" = 0.25, "A_Q75" = 0.75),
          summary_quantiles = c("S_Q05" = 0.05, "S_Q95" = 0.95))
# Use example boundary data from package
region_boundary <- geo_tox_data$boundaries$county
group_boundary <- geo_tox_data$boundaries$state
n <- nrow(region_boundary)

# Single assay quantile
df <- data.frame(id = region_boundary$FIPS,
                 metric = "GCA.Eff",
                 assay_quantile = 0.5,
                 value = runif(n)^3)
# Default plot
plot_resp(df, region_boundary)
# Add group boundary, a state border in this case
plot_resp(df, region_boundary, group_boundary)
# Change quantile label
plot_resp(df, region_boundary, group_boundary,
          assay_quantiles = c("Q50" = 0.5))

# Multiple assay quantiles
df <- data.frame(id = rep(region_boundary$FIPS, 2),
                 metric = "GCA.Eff",
                 assay_quantile = rep(c(0.25, 0.75), each = n),
                 value = c(runif(n)^3, runif(n)^3 + 0.15))
plot_resp(df, region_boundary, group_boundary,
          assay_quantiles = c("Q25" = 0.25, "Q75" = 0.75))

# Summary quantiles
df <- data.frame(id = rep(region_boundary$FIPS, 4),
                 assay_quantile = rep(rep(c(0.25, 0.75), each = n), 2),
                 summary_quantile = rep(c(0.05, 0.95), each = n * 2),
                 metric = "GCA.Eff",
                 value = c(runif(n)^3, runif(n)^3 + 0.15,
                           runif(n)^3 + 0.7, runif(n)^3 + 0.85))
plot_resp(df, region_boundary, group_boundary,
          assay_quantiles = c("A_Q25" = 0.25, "A_Q75" = 0.75),
          summary_quantiles = c("S_Q05" = 0.05, "S_Q95" = 0.95))

Plot results of sensitivity analysis.

Description

Plot results of sensitivity analysis.

Usage

plot_sensitivity(
  x,
  metric = "GCA.Eff",
  assay = NULL,
  y = "",
  xlab = metric,
  ylab = ""
)
plot_sensitivity(
  x,
  metric = "GCA.Eff",
  assay = NULL,
  y = "",
  xlab = metric,
  ylab = ""
)

Arguments

`x`	GeoTox object.
`metric`	metric to plot. Valid choices are "GCA.Eff", "IA.Eff", "GCA.HQ.10", and "IA.HQ.10".
`assay`	which assay to plot, if multiple exist.
`y`	y value or text for bottom of ridge plot.
`xlab`	x-axis label.
`ylab`	y-axis label.

Value

ggplot2 object.

Examples

# Required GeoTox fields are generated by first running [calculate_response]
# and [sensitivity_analysis] on a GeoTox object. This will create the fields
# `resp` and `sensitivity`. For this example, dummy data will be used.
make_data <- function(n = 5, metric = "GCA.Eff") {
  list(stats::setNames(data.frame(1:n, runif(n)),
                       c("sample", metric)))
}

geoTox <- GeoTox()
geoTox$resp <- make_data()
geoTox$sensitivity <- list(age = make_data(),
                           obesity = make_data(),
                           css_params = make_data(),
                           fit_params = make_data(),
                           C_ext = make_data())

plot_sensitivity(geoTox)
# Required GeoTox fields are generated by first running [calculate_response]
# and [sensitivity_analysis] on a GeoTox object. This will create the fields
# `resp` and `sensitivity`. For this example, dummy data will be used.
make_data <- function(n = 5, metric = "GCA.Eff") {
  list(stats::setNames(data.frame(1:n, runif(n)),
                       c("sample", metric)))
}

geoTox <- GeoTox()
geoTox$resp <- make_data()
geoTox$sensitivity <- list(age = make_data(),
                           obesity = make_data(),
                           css_params = make_data(),
                           fit_params = make_data(),
                           C_ext = make_data())

plot_sensitivity(geoTox)

Get response quantiles

Description

Get response quantiles

Usage

resp_quantiles(
  resp,
  metric = c("GCA.Eff", "IA.Eff", "GCA.HQ.10", "IA.HQ.10"),
  assays = NULL,
  assay_summary = FALSE,
  assay_quantiles = c(Median = 0.5),
  summary_quantiles = c(`10th percentile` = 0.1)
)
resp_quantiles(
  resp,
  metric = c("GCA.Eff", "IA.Eff", "GCA.HQ.10", "IA.HQ.10"),
  assays = NULL,
  assay_summary = FALSE,
  assay_quantiles = c(Median = 0.5),
  summary_quantiles = c(`10th percentile` = 0.1)
)

Arguments

`resp`	calculated mixture response output from calc_concentration_response.
`metric`	response metric, one of "GCA.Eff", "IA.Eff", "GCA.HQ.10" or "IA.HQ.10".
`assays`	assays to summarize. If NULL and multiple assays exist, then the first assay is used.
`assay_summary`	boolean indicating whether to summarize across assays.
`assay_quantiles`	numeric vector of assay quantiles.
`summary_quantiles`	numeric vector of quantiles to compute across all assay quantiles.

Details

The columns of the returned data frame will vary based on the inputs. If assays is specified and assay_summary is FALSE, then the resulting data frame will have an assay column. If assay_summary is TRUE, then the data frame will have an summary_quantile column.

Value

data frame with computed response quantiles.

Examples

# Dummy response data
resp <- list(
  "r1" = data.frame(assay = c("a1", "a1", "a2", "a2"),
                    sample = c(1, 2, 1, 2),
                    GCA.Eff = c(1, 2, 3, 4),
                    IA.Eff = c(5, 6, 7, 8),
                    "GCA.HQ.10" = c(9, 10, 11, 12),
                    "IA.HQ.10" = c(13, 14, 15, 16)))

# Summarize single assay
resp_quantiles(resp)
# Specify assay
resp_quantiles(resp, assays = "a1")
# Specify quantiles
resp_quantiles(resp, assays = "a1", assay_quantiles = c(0.25, 0.75))
# Specify metric
resp_quantiles(resp, assays = "a1", metric = "IA.HQ.10")

# Summarize across assays
resp_quantiles(resp, assay_summary = TRUE)
# Specify quantiles
suppressWarnings(
  resp_quantiles(resp,
                 assay_summary = TRUE,
                 assay_quantiles = c(0.25, 0.75),
                 summary_quantiles = c(0.1, 0.9))
)
# Dummy response data
resp <- list(
  "r1" = data.frame(assay = c("a1", "a1", "a2", "a2"),
                    sample = c(1, 2, 1, 2),
                    GCA.Eff = c(1, 2, 3, 4),
                    IA.Eff = c(5, 6, 7, 8),
                    "GCA.HQ.10" = c(9, 10, 11, 12),
                    "IA.HQ.10" = c(13, 14, 15, 16)))

# Summarize single assay
resp_quantiles(resp)
# Specify assay
resp_quantiles(resp, assays = "a1")
# Specify quantiles
resp_quantiles(resp, assays = "a1", assay_quantiles = c(0.25, 0.75))
# Specify metric
resp_quantiles(resp, assays = "a1", metric = "IA.HQ.10")

# Summarize across assays
resp_quantiles(resp, assay_summary = TRUE)
# Specify quantiles
suppressWarnings(
  resp_quantiles(resp,
                 assay_summary = TRUE,
                 assay_quantiles = c(0.25, 0.75),
                 summary_quantiles = c(0.1, 0.9))
)

Sample from pre-generated `C_ss` data

Description

Sample from pre-generated C_ss data

Usage

sample_Css(simulated_css, age, obesity)
sample_Css(simulated_css, age, obesity)

Arguments

`simulated_css`	list of pre-generated `C_ss` data, for details see: `vignette("package_data", package = "GeoTox")`.
`age`	list or atomic vector of ages.
`obesity`	list or atomic vector of obesity status.

Value

list of matrices containing C_ss values. Columns are sorted to have consistent order across functions.

Examples

# Vector inputs
sample_Css(geo_tox_data$simulated_css,
           c(15, 25, 35),
           c("Normal", "Obese", "Normal"))

# List inputs
sample_Css(geo_tox_data$simulated_css,
           list(c(34, 29), 55),
           list(c("Obese", "Normal"), "Normal"))
# Vector inputs
sample_Css(geo_tox_data$simulated_css,
           c(15, 25, 35),
           c("Normal", "Obese", "Normal"))

# List inputs
sample_Css(geo_tox_data$simulated_css,
           list(c(34, 29), 55),
           list(c("Obese", "Normal"), "Normal"))

Perform sensitivity analysis

Description

Perform sensitivity analysis

Usage

sensitivity_analysis(x, max_mult = list(NULL, NULL, NULL, 1.2, NULL))
sensitivity_analysis(x, max_mult = list(NULL, NULL, NULL, 1.2, NULL))

Arguments

`x`	GeoTox object.
`max_mult`	numeric list of length 5 for each step of the sensitivity analysis.

Details

This wrapper function will sequentially call the compute_sensitivity function with inputs age, obesity, css_params, fit_params, and C_ext. The results will be returned as a named list and stored in the sensitivity field of the input GeoTox object.

Values of NULL in the max_mult input will use the default value stored in the GeoTox object (x$par$resp$max_mult). When a GeoTox object is created this is initialized at 1.5, but can be changed via the calculate_response function or directly in the object.

Value

The same GeoTox object with added sensitivity field.

Examples

# Use a subset of the package data for demonstration purposes
set.seed(2357)
n <- 10 # Population size
m <- 5 # Number of regions
idx <- if (m < 100) sample(1:100, m) else 1:100

# Create GeoTox object and populate required fields
geoTox <- GeoTox() |>
  # Simulate populations for each region
  simulate_population(age = split(geo_tox_data$age, ~FIPS)[idx],
                      obesity = geo_tox_data$obesity[idx, ],
                      exposure = split(geo_tox_data$exposure, ~FIPS)[idx],
                      simulated_css = geo_tox_data$simulated_css,
                      n = n) |>
  # Estimated Hill parameters
  set_hill_params(geo_tox_data$dose_response |>
                    fit_hill(assay = "endp", chem = "casn") |>
                    dplyr::filter(!tp.sd.imputed, !logAC50.sd.imputed))

# Sensitivity analysis can now be done
geoTox <- geoTox |> sensitivity_analysis()
# Use a subset of the package data for demonstration purposes
set.seed(2357)
n <- 10 # Population size
m <- 5 # Number of regions
idx <- if (m < 100) sample(1:100, m) else 1:100

# Create GeoTox object and populate required fields
geoTox <- GeoTox() |>
  # Simulate populations for each region
  simulate_population(age = split(geo_tox_data$age, ~FIPS)[idx],
                      obesity = geo_tox_data$obesity[idx, ],
                      exposure = split(geo_tox_data$exposure, ~FIPS)[idx],
                      simulated_css = geo_tox_data$simulated_css,
                      n = n) |>
  # Estimated Hill parameters
  set_hill_params(geo_tox_data$dose_response |>
                    fit_hill(assay = "endp", chem = "casn") |>
                    dplyr::filter(!tp.sd.imputed, !logAC50.sd.imputed))

# Sensitivity analysis can now be done
geoTox <- geoTox |> sensitivity_analysis()

Set GeoTox boundaries

Description

Set GeoTox boundaries

Usage

set_boundaries(x, region = NULL, group = NULL)
set_boundaries(x, region = NULL, group = NULL)

Arguments

`x`	GeoTox object.
`region`	"sf" data.frame mapping features to a "geometry" column. Used when coloring map regions.
`group`	"sf" data.frame containing a "geometry" column. Used to draw outlines around groups of regions.

Value

same GeoTox object with boundaries set.

Examples

geoTox <- GeoTox() |> 
  set_boundaries(region = geo_tox_data$boundaries$county,
                 group  = geo_tox_data$boundaries$state)
geoTox <- GeoTox() |> 
  set_boundaries(region = geo_tox_data$boundaries$county,
                 group  = geo_tox_data$boundaries$state)

Set Hill parameters for a GeoTox object.

Description

Set Hill parameters for a GeoTox object.

Usage

set_hill_params(x, hill_params)
set_hill_params(x, hill_params)

Arguments

`x`	GeoTox object.
`hill_params`	output of fit_hill.

Value

same GeoTox object with Hill parameters set.

Examples

hill_params <- geo_tox_data$dose_response |>
  fit_hill(chem = "casn", assay = "endp") |>
  dplyr::filter(!tp.sd.imputed, !logAC50.sd.imputed)

geoTox <- GeoTox() |> 
  set_hill_params(hill_params)
hill_params <- geo_tox_data$dose_response |>
  fit_hill(chem = "casn", assay = "endp") |>
  dplyr::filter(!tp.sd.imputed, !logAC50.sd.imputed)

geoTox <- GeoTox() |> 
  set_hill_params(hill_params)

Simulate ages

Description

Simulate ages

Usage

simulate_age(x, n = 1000)
simulate_age(x, n = 1000)

Arguments

`x`	data frame or list of data frames containing population data for age groups. Each data frame must contain columns "AGEGRP" and "TOT_POP".
`n`	simulated sample size.

Details

Each data frame must contain 19 rows. The first row represents the total population of all age groups while the next 18 rows represent age groups from 0 to 89 in increments of 5 years.

Value

List of arrays containing simulated ages.

Examples

# Single data frame
x <- data.frame(AGEGRP = 0:18, TOT_POP = 0)
# populate only age range 40-44, set population total of all ages
x$TOT_POP[c(1, 10)] <- 100
simulate_age(x, 5)

# List of 2 data frames
y <- data.frame(AGEGRP = 0:18, TOT_POP = 0)
# populate age ranges 5-9 and 50-54
y$TOT_POP[c(3, 12)] <- 10
# set population total for all age groups
y$TOT_POP[1] <- sum(y$TOT_POP)
simulate_age(list(x = x, y = y), 15)

# Single data frame
x <- data.frame(AGEGRP = 0:18, TOT_POP = 0)
# populate only age range 40-44, set population total of all ages
x$TOT_POP[c(1, 10)] <- 100
simulate_age(x, 5)

# List of 2 data frames
y <- data.frame(AGEGRP = 0:18, TOT_POP = 0)
# populate age ranges 5-9 and 50-54
y$TOT_POP[c(3, 12)] <- 10
# set population total for all age groups
y$TOT_POP[1] <- sum(y$TOT_POP)
simulate_age(list(x = x, y = y), 15)

Simulate external exposure

Description

Simulate external exposure

Usage

simulate_exposure(
  x,
  expos_mean = "mean",
  expos_sd = "sd",
  expos_label = "casn",
  n = 1000
)
simulate_exposure(
  x,
  expos_mean = "mean",
  expos_sd = "sd",
  expos_label = "casn",
  n = 1000
)

Arguments

`x`	data frame or list of data frames containing exposure data.
`expos_mean`	column name of mean values.
`expos_sd`	column name of standard deviations.
`expos_label`	column name of labeling term, required if `x` has more than one row.
`n`	simulated sample size.

Value

list of matrices containing inhalation rates. Matrix columns are named using the values in the expos_label column for more than one data frame row. Columns are sorted to have consistent order across functions.

Examples

# Single data frame
x <- data.frame(mean = 1:3, sd = (1:3) / 10, casn = letters[1:3])
simulate_exposure(x, n = 5)

# List of 2 data frames
y <- data.frame(mean = 4:6, sd = 0.1, casn = letters[1:3])
simulate_exposure(list(loc1 = x, loc2 = y), n = 5)

# Input has custom column names
z <- data.frame(ave = 1:3, stdev = (1:3) / 10, chnm = letters[1:3])
simulate_exposure(z,
                  expos_mean = "ave",
                  expos_sd = "stdev",
                  expos_label = "chnm",
                  n = 5)

# Single data frame
x <- data.frame(mean = 1:3, sd = (1:3) / 10, casn = letters[1:3])
simulate_exposure(x, n = 5)

# List of 2 data frames
y <- data.frame(mean = 4:6, sd = 0.1, casn = letters[1:3])
simulate_exposure(list(loc1 = x, loc2 = y), n = 5)

# Input has custom column names
z <- data.frame(ave = 1:3, stdev = (1:3) / 10, chnm = letters[1:3])
simulate_exposure(z,
                  expos_mean = "ave",
                  expos_sd = "stdev",
                  expos_label = "chnm",
                  n = 5)

Simulate inhalation rates

Description

Simulate inhalation rates

Usage

simulate_inhalation_rate(x, IR_params = NULL)
simulate_inhalation_rate(x, IR_params = NULL)

Arguments

`x`	atomic vector or list of atomic vectors containing ages.
`IR_params`	(optional) data frame with columns "age", "mean" and "sd". See details for more information.

Details

The age column of the optional IR_params data frame should be in ascending order and represent the lower value of age groups for the corresponding mean and sd values. When not provided, the default values will come from Table 6.7 of EPA's 2011 Exposure Factors Handbook using the mean of male and female values.

Value

List of atomic vectors containing inhalation rates.

Examples

# Single atomic vector
ages <- sample(1:100, 6, replace = TRUE)
simulate_inhalation_rate(ages)

# List of atomic vectors
ages <- list(
  sample(1:100, 5, replace = TRUE),
  sample(1:100, 3, replace = TRUE)
)
simulate_inhalation_rate(ages)

# Custom IR_params
IR_params <- data.frame("age" = c(0, 20, 50),
                        "mean" = c(0.5, 0.3, 0.2),
                        "sd" = c(0.1, 0.06, 0.03))
simulate_inhalation_rate(c(15, 30, 65), IR_params)

# Single atomic vector
ages <- sample(1:100, 6, replace = TRUE)
simulate_inhalation_rate(ages)

# List of atomic vectors
ages <- list(
  sample(1:100, 5, replace = TRUE),
  sample(1:100, 3, replace = TRUE)
)
simulate_inhalation_rate(ages)

# Custom IR_params
IR_params <- data.frame("age" = c(0, 20, 50),
                        "mean" = c(0.5, 0.3, 0.2),
                        "sd" = c(0.1, 0.06, 0.03))
simulate_inhalation_rate(c(15, 30, 65), IR_params)

Simulate obesity status

Description

Simulate obesity status

Usage

simulate_obesity(
  x,
  obes_prev = "OBESITY_CrudePrev",
  obes_sd = "OBESITY_SD",
  obes_label = "FIPS",
  n = 1000
)
simulate_obesity(
  x,
  obes_prev = "OBESITY_CrudePrev",
  obes_sd = "OBESITY_SD",
  obes_label = "FIPS",
  n = 1000
)

Arguments

`x`	data frame containing obesity data as a percentage from 0 to 100.
`obes_prev`	column name of prevalence.
`obes_sd`	column name of standard deviation.
`obes_label`	column name of labeling term, required if `x` has more than one row.
`n`	simulated sample size.

Value

List of arrays containing simulated obesity status.

Examples

# Input has default column names
df <- data.frame(OBESITY_CrudePrev = c(20, 50, 80),
                 OBESITY_SD = c(5, 5, 5),
                 FIPS = letters[1:3])
simulate_obesity(df, n = 5)

# Input has custom column names
df <- data.frame(prev = c(20, 50, 80),
                 sd = c(5, 5, 5),
                 label = letters[1:3])
simulate_obesity(df,
                 obes_prev = "prev",
                 obes_sd = "sd",
                 obes_label = "label",
                 n = 5)

# Input has default column names
df <- data.frame(OBESITY_CrudePrev = c(20, 50, 80),
                 OBESITY_SD = c(5, 5, 5),
                 FIPS = letters[1:3])
simulate_obesity(df, n = 5)

# Input has custom column names
df <- data.frame(prev = c(20, 50, 80),
                 sd = c(5, 5, 5),
                 label = letters[1:3])
simulate_obesity(df,
                 obes_prev = "prev",
                 obes_sd = "sd",
                 obes_label = "label",
                 n = 5)

Simulate population data

Description

Simulate population data for given input fields

Usage

simulate_population(
  x,
  age = NULL,
  obesity = NULL,
  exposure = NULL,
  simulated_css = NULL,
  ...
)
simulate_population(
  x,
  age = NULL,
  obesity = NULL,
  exposure = NULL,
  simulated_css = NULL,
  ...
)

Arguments

`x`	GeoTox object.
`age`	input `x` to function simulate_age. After simulating ages, the inhalation rate is subsequently calculated using simulate_inhalation_rate.
`obesity`	input `x` to function simulate_obesity.
`exposure`	input `x` to function simulate_exposure.
`simulated_css`	input `simulated_css` to functions sample_Css and get_fixed_css.
`...`	additional arguments passed to other functions. See details.

Details

Additional parameters include n for sample size, IR_params for simulate_inhalation_rate, obes_prev, obes_sd, and obes_label for simulate_obesity, and expos_mean, expos_sd, and expos_label for simulate_exposure.

Value

The same object with simulated fields added.

Examples

# Use a subset of the package data for demonstration purposes
set.seed(2357)
n <- 10 # Population size
m <- 5 # Number of regions
idx <- if (m < 100) sample(1:100, m) else 1:100

# Create GeoTox object
geoTox <- GeoTox() |>
  # Simulate populations for each region
  simulate_population(age = split(geo_tox_data$age, ~FIPS)[idx],
                      obesity = geo_tox_data$obesity[idx, ],
                      exposure = split(geo_tox_data$exposure, ~FIPS)[idx],
                      simulated_css = geo_tox_data$simulated_css,
                      n = n)
# Use a subset of the package data for demonstration purposes
set.seed(2357)
n <- 10 # Population size
m <- 5 # Number of regions
idx <- if (m < 100) sample(1:100, m) else 1:100

# Create GeoTox object
geoTox <- GeoTox() |>
  # Simulate populations for each region
  simulate_population(age = split(geo_tox_data$age, ~FIPS)[idx],
                      obesity = geo_tox_data$obesity[idx, ],
                      exposure = split(geo_tox_data$exposure, ~FIPS)[idx],
                      simulated_css = geo_tox_data$simulated_css,
                      n = n)

Package 'GeoTox'

Help Index

Calculate the mixture response from one of three different approaches: IA, GCA, or Hazard Quotient

Description

Usage

Arguments

Value

Examples

Independent Action

Description

Usage

Arguments

Details

Value

See Also

Examples

Calculate internal chemical dose

Description

Usage

Arguments

Details

Value

Examples

Calculate in vitro concentration

Description

Usage

Arguments

Details

Value

Examples

Calculate response

Description

Usage

Arguments

Details

Value

See Also

Examples

Compute response sensitivity to parameter variation.

Description

Usage

Arguments

Value

Examples

Fit 2- or 3-parameter Hill model

Description

Usage

Arguments

Details

Value

Examples

GeoTox Data

Description

Usage

Format

GeoTox S3 object

Description

Usage

Arguments

Value

See Also

Examples

Get C_ss Data for Fixed Age

Description

Usage

Arguments

Value

Examples

Get Fixed C_ss Data

Description

Usage

Arguments

Value

Examples

Get C_ss Data for Fixed Obesity Status

Description

Usage

Arguments

Value

Examples

Get `C_ss` Data for Fixed Age

Get Fixed `C_ss` Data

Get `C_ss` Data for Fixed Obesity Status

Get median `C_ss` Values

Get `C_ss` Data for Fixed `C_ss` Generation Parameters

Sample from pre-generated `C_ss` data