Package 'GeoTox'

Add age simulation data

Description

Create or add to the 'age' table in a GeoTox database.

Usage

add_age(GT, df, location = "FIPS")

Arguments

GT	GeoTox object.
df	Data frame with age simulation data.
location	Column name(s) in df that contain location identifier(s) (default "FIPS").

Details

The simulation data must have columns "AGEGRP" and "TOT_POP" and at least one column containing location information (default "FIPS"). Each location must have 19 rows for AGEGRP 0-18, where 1-18 are age groups in increments of 5, e.g. AGEGRP = 5 would be ages 20 to 24, and AGEGRP = 0 is the combination of all age groups. The age data is used by simulate_age() to generate age samples for each location.

The location input can be a named vector to specify multiple identifier columns in df. For example, location = c(FIPS = "FIPS", state = "ST") would indicate that df contains both FIPS codes and state identifiers for locations. The state = "ST" part would rename the "ST" column in df to "state" in the 'location' table.

Value

The same GeoTox object, invisibly.

See Also

simulate_age()

Examples

# Example age simulation data
age_df <- data.frame(
  FIPS = rep(c(10000, 20000), each = 19),
  AGEGRP = rep(0:18, times = 2),
  TOT_POP = 0
)
# FIPS 10000, populate age group 40-44
age_df$TOT_POP[c(1, 10)] = 100
# FIPS 20000, populate age groups 50-59
age_df$TOT_POP[c(1, 12, 13) + 19] = c(200, 100, 100)

# Add age simulation data to GeoTox database
GT <- GeoTox() |> add_age(age_df)

# Open a connection to GeoTox database
con <- get_con(GT)

# Look at created tables

dplyr::tbl(con, "age") |> dplyr::filter(TOT_POP > 0) |> dplyr::collect()

dplyr::tbl(con, "location") |> dplyr::collect()

# Clean up example
DBI::dbDisconnect(con)
file.remove(GT$db_info$dbdir)

---

Add exposure simulation data

Description

Create or add to the 'exposure' table in a GeoTox database.

Usage

add_exposure(GT, df, location = "FIPS", substance = "casn", route = "route")

Arguments

GT	GeoTox object.
df	Data frame with exposure simulation data.
location	Column name(s) in df that contain location identifier(s) (default "FIPS").
substance	Column name(s) in df that contain substance identifier(s) (default "casn").
route	Column name in df that contains route identifier (default "route").

Details

The simulation data must contain columns for exposure mean and standard deviation (default "mean" and "sd", respectfully), at least one column containing location information (default "FIPS"), at least one column containing substance information (default "casn"), and one column containing route information (default "route"). The exposure data is used by simulate_exposure() to generate external exposure concentration samples for each location.

The location and substance inputs can be named vectors to specify multiple identifier columns in df. For example, substance = c(casn = "casn", name = "chnm") would indicate that df contains both CAS numbers and chemical names for substances. The name = "chnm" part would rename the "chnm" column in df to "name" in the 'substance' table.

Value

The same GeoTox object, invisibly.

See Also

simulate_exposure()

Examples

# Example exposure simulation data
exposure_df <- tibble::tribble(
  ~FIPS, ~casn, ~route, ~mean, ~sd,
  10000, "00-00-1", "inhalation", 10, 1,
  10000, "00-00-2", "inhalation", 20, 1,
  20000, "00-00-1", "inhalation", 30, 1,
  20000, "00-00-2", "inhalation", 40, 1
)

# Add exposure simulation data to GeoTox database
GT <- GeoTox() |> add_exposure(exposure_df)

# Open a connection to GeoTox database
con <- get_con(GT)

# Look at created tables
dplyr::tbl(con, "exposure") |> dplyr::collect()
dplyr::tbl(con, "location") |> dplyr::collect()
dplyr::tbl(con, "substance") |> dplyr::collect()
dplyr::tbl(con, "route") |> dplyr::collect()

# Add another substance with a new field and route
exposure_df <- tibble::tribble(
  ~FIPS, ~casn, ~chnm, ~route, ~mean, ~sd,
  10000, "00-00-3", "chem3", "drinking", 100, 1,
  20000, "00-00-3", "chem3", "drinking", 200, 1
)
GT |> add_exposure(exposure_df, substance = c(casn = "casn", name = "chnm"))

# Look at updated tables

dplyr::tbl(con, "exposure") |> dplyr::collect()

dplyr::tbl(con, "substance") |> dplyr::collect()

dplyr::tbl(con, "route") |> dplyr::collect()

# Clean up example
DBI::dbDisconnect(con)
file.remove(GT$db_info$dbdir)

---

Add exposure rate simulation data

Description

Create or add to the 'exposure_rate_params' table in a GeoTox database.

Usage

add_exposure_rate_params(
  GT,
  route = "inhalation",
  params = NULL,
  overwrite = FALSE
)

Arguments

GT	GeoTox object.
route	Exposure route (default "inhalation").
params	Data frame with exposure rate parameters. If NULL, default parameters for the specified route will be used (default NULL).
overwrite	Logical indicating whether to overwrite existing exposure rate parameters for the specified route (default FALSE).

Details

There are two routes with built-in default exposure rate parameters: "inhalation" and "drinking". If params is not provided (NULL), the default parameters for the specified route will be used. If params is provided, it must contain columns "age_lb", "age_ub", "mean", and "sd". The exposure rate parameters are used by simulate_exposure_rate() to generate exposure rate samples for each individual in the population.

The build-in parameters come from the following sources:

• Inhalation: EPA Exposure Factors Handbook (2015), Table 6.7 in m$^3$/kg-day. The "mean" and "sd" values are the average of male and female values.

• Drinking: EPA Exposure Factors Handbook (2019), Table 3-30 in mL/kg-day.

Value

The same GeoTox object, invisibly.

See Also

simulate_exposure_rate()

Examples

# Add both default params to GeoTox database
GT <- GeoTox() |>
  add_exposure_rate_params() |>
  add_exposure_rate_params(route = "drinking")

# Open a connection to GeoTox database
con <- get_con(GT)

# Look at relevant tables
params_tbl <- dplyr::tbl(con, "exposure_rate_params") |> dplyr::collect()

params_tbl |> dplyr::filter(route_id == 1)

params_tbl |> dplyr::filter(route_id == 2)

dplyr::tbl(con, "route") |> dplyr::collect()

# Add custom params with new column (gender), assign to route "custom"
params_df <- tibble::tribble(
  ~age_lb, ~age_ub, ~gender, ~mean, ~sd,
   0, 49, "male",   10, 1,
  50, 99, "male",   20, 1,
   0, 49, "female", 30, 1,
  50, 99, "female", 40, 1
)
GT |> add_exposure_rate_params(params_df, route = "custom")

# Look at updated tables
params_tbl <- dplyr::tbl(con, "exposure_rate_params") |> dplyr::collect()

params_tbl |> dplyr::filter(route_id == 1)

params_tbl |> dplyr::filter(route_id == 2)

params_tbl |> dplyr::filter(route_id == 3)

dplyr::tbl(con, "route") |> dplyr::collect()

# Clean up example
DBI::dbDisconnect(con)
file.remove(GT$db_info$dbdir)

---

Add Hill model parameters

Description

Create or add to the 'hill_params' table in a GeoTox database.

Usage

add_hill_params(GT, hill_params)

Arguments

GT	GeoTox object.
hill_params	List output from fit_hill().

Value

The same GeoTox object, invisibly.

See Also

fit_hill()

Examples

# Example Hill model data
hill_df <- tibble::tribble(
  ~assay, ~model, ~casn, ~logc, ~resp,
  "a1", "human", "00-00-1",    0,  10,
  "a1", "human", "00-00-1",    1,  20,
  "a1", "human", "00-00-1",    2,  80,
  "a1", "human", "00-00-1",    3, 100,
  "a1", "human", "00-00-2", -0.5,   5,
  "a1", "human", "00-00-2",  0.5,  20,
  "a1", "human", "00-00-2",  1.5,  55,
  "a1", "human", "00-00-2",  2.5,  60,
  "a2",   "rat", "00-00-1",   -1,   0,
  "a2",   "rat", "00-00-1",    0,  10,
  "a2",   "rat", "00-00-1",    1,  30,
  "a2",   "rat", "00-00-1",    2,  40
)
hill_params <- fit_hill(
  hill_df, assay = c(name = "assay", model = "model"), substance = "casn"
)

# Add Hill model parameters to GeoTox database
GT <- GeoTox() |> add_hill_params(hill_params)

# Open a connection to GeoTox database
con <- get_con(GT)

# Look at created tables

dplyr::tbl(con, "hill_params") |> dplyr::collect()

dplyr::tbl(con, "assay") |> dplyr::collect()

dplyr::tbl(con, "substance") |> dplyr::collect()

# Clean up example
DBI::dbDisconnect(con)
file.remove(GT$db_info$dbdir)

---

Add obesity simulation data

Description

Create or add to the 'obesity' table in a GeoTox database.

Usage

add_obesity(GT, df, location = "FIPS")

Arguments

GT	GeoTox object.
df	Data frame with obesity simulation data.
location	Column name(s) in df that contain location identifier(s) (default "FIPS").

Details

The simulation data must contain columns for obesity prevalence and standard deviation (default "OBESITY_CrudePrev" and "OBESITY_SD", respectfully) and at least one column containing location information (default "FIPS"). The obesity data is used by simulate_obesity() to generate weight category samples for each location.

The location input can be a named vector to specify multiple identifier columns in df. For example, location = c(FIPS = "FIPS", state = "ST") would indicate that df contains both FIPS codes and state identifiers for locations. The state = "ST" part would rename the "ST" column in df to "state" in the 'location' table.

Value

The same GeoTox object, invisibly.

See Also

simulate_obesity()

Examples

# Example obesity simulation data
obesity_df <- data.frame(
  FIPS = c(10000, 20000),
  OBESITY_CrudePrev = c(20, 80),
  OBESITY_SD = 5
)

# Add obesity simulation data to GeoTox database
GT <- GeoTox() |> add_obesity(obesity_df)

# Open a connection to GeoTox database
con <- get_con(GT)

# Look at created tables

dplyr::tbl(con, "obesity") |> dplyr::collect()

dplyr::tbl(con, "location") |> dplyr::collect()

# Add another location with additional information
obesity_df <- data.frame(
 FIPS = 30000,
 ST = "State3",
 OBESITY_CrudePrev = 50,
 OBESITY_SD = 10
)
GT |> add_obesity(obesity_df, location = c(FIPS = "FIPS", state = "ST"))

# Look at updated tables

dplyr::tbl(con, "obesity") |> dplyr::collect()

dplyr::tbl(con, "location") |> dplyr::collect()

# Clean up example
DBI::dbDisconnect(con)
file.remove(GT$db_info$dbdir)

---

Calculate internal dose

Description

Calculates internal dose (D_int) as the product of external concentration ('C_ext' in the 'concentration' table) and exposure rate ('rate' in the 'exposure_rate' table), and stores the results in the 'D_int' column of the 'concentration' table in the GeoTox database.

Usage

calc_internal_dose(GT, overwrite = FALSE, sensitivity = FALSE)

Arguments

GT	GeoTox object.
overwrite	Logical indicating whether to overwrite existing 'D_int' values in the 'concentration' table (default FALSE).
sensitivity	Logical indicating whether to simulate internal dose for sensitivity analysis (default FALSE).

Details

If sensitivity = TRUE, 'D_int' will be calculated for sensitivity analysis. Typically this shouldn't be used directly by the user, but rather called by calc_sensitivity(). In this case, the function will use the 'concentration_sensitivity' and 'exposure_rate_sensitivity' tables instead of the 'concentration' and 'exposure_rate' tables.

Value

The same GeoTox object, invisibly.

See Also

calc_response()

Examples

# Setup required tables
sample_df <- tibble::tribble(
  ~FIPS, ~age, ~weight,
  10000, 25, "Normal",
  10000, 35,  "Obese",
  20000, 50, "Normal"
)
exposure_df <- tibble::tribble(
  ~FIPS, ~casn, ~route, ~mean, ~sd,
  10000, "00-00-1", "inhalation", 10, 1,
  10000, "00-00-2", "inhalation", 20, 1,
  20000, "00-00-1", "inhalation", 30, 1,
  20000, "00-00-2", "inhalation", 40, 1
)
GT <- GeoTox() |>
  set_sample(sample_df) |>
  add_exposure_rate_params() |>
  simulate_population(exposure = exposure_df, sample_css = FALSE)

# Calculate internal dose
GT <- GT |> calc_internal_dose()

# Open a connection to GeoTox database
con <- get_con(GT)

# Look at relevant tables

dplyr::tbl(con, "concentration") |> dplyr::collect()

dplyr::tbl(con, "exposure_rate") |> dplyr::collect()

# Clean up example
DBI::dbDisconnect(con)
file.remove(GT$db_info$dbdir)

---

Calculate in vitro concentration

Description

Calculates the in vitro concentration (C_invitro) as the product of steady state plasma concentration (C_ss) and internal dose (D_int), and stores the results in the 'C_invitro' column of the 'concentration' table in the GeoTox database.

Usage

calc_invitro_concentration(GT, overwrite = FALSE, sensitivity = FALSE)

Arguments

GT	GeoTox object.
overwrite	Logical indicating whether to overwrite existing 'C_invitro' values in the 'concentration' table (default FALSE).
sensitivity	Logical indicating whether to simulate in vitro concentration for sensitivity analysis (default FALSE).

Details

If sensitivity = TRUE, 'C_invitro' will be calculated for sensitivity analysis. Typically this shouldn't be used directly by the user, but rather called by calc_sensitivity(). In this case, the function will use the 'concentration_sensitivity' table instead of the 'concentration' table.

Value

The same GeoTox object, invisibly.

See Also

calc_internal_dose(), calc_response()

Examples

# Setup required tables
sample_df <- tibble::tribble(
  ~FIPS, ~age, ~weight,
  10000, 25, "Normal",
  10000, 35,  "Obese",
  20000, 50, "Normal"
)
exposure_df <- tibble::tribble(
  ~FIPS, ~casn, ~route, ~mean, ~sd,
  10000, "00-00-1", "inhalation", 10, 1,
  10000, "00-00-2", "inhalation", 20, 1,
  20000, "00-00-1", "inhalation", 30, 1,
  20000, "00-00-2", "inhalation", 40, 1
)
css_df <- tibble::tribble(
  ~casn, ~age_lb, ~age_ub, ~weight, ~css,
  "00-00-1",  0, 49, "Normal", 21,
  "00-00-1", 50, 99, "Normal", 22,
  "00-00-1",  0, 49,  "Obese", 61,
  "00-00-1", 50, 99,  "Obese", 62,
  "00-00-2",  0, 49, "Normal", 11,
  "00-00-2", 50, 99, "Normal", 12,
  "00-00-2",  0, 49,  "Obese", 31,
  "00-00-2", 50, 99,  "Obese", 32
)
GT <- GeoTox() |>
  set_sample(sample_df) |>
  set_simulated_css(css_df) |>
  add_exposure_rate_params() |>
  simulate_population(exposure = exposure_df) |>
  calc_internal_dose()

# Calculate in vitro concentration
GT <- GT |> calc_invitro_concentration()

# Open a connection to GeoTox database
con <- get_con(GT)

# Look at relevant tables
dplyr::tbl(con, "concentration") |> dplyr::collect()

# Clean up example
DBI::dbDisconnect(con)
file.remove(GT$db_info$dbdir)

---

Calculate concentrations and risks

Description

Calculate internal dose, in vitro concentration, and risk estimates.

Usage

calc_response(GT, overwrite = FALSE, ...)

Arguments

GT	GeoTox object.
overwrite	Logical indicating whether to overwrite existing values (default FALSE).
...	Additional arguments passed to calc_risk().

Details

This is a wrapper around several other functions:

• calc_internal_dose() to calculate internal dose (D_int).

• calc_invitro_concentration() to calculate in vitro concentration (C_invitro).

• calc_risk() to calculate risk estimates.

If a risk_name argument is provided to ... and it is not "risk", then sensitivity analysis is assumed and the sensitivity argument in the internal dose and in vitro concentration calculations is set to TRUE.

Value

The same GeoTox object, invisibly.

See Also

calc_internal_dose(), calc_invitro_concentration(), calc_risk()

Examples

# Setup required tables
sample_df <- tibble::tribble(
  ~FIPS, ~age, ~weight,
  10000, 25, "Normal",
  10000, 35,  "Obese",
  20000, 50, "Normal"
)
exposure_df <- tibble::tribble(
  ~FIPS, ~casn, ~route, ~mean, ~sd,
  10000, "00-00-1", "inhalation", 10, 1,
  10000, "00-00-2", "inhalation", 20, 1,
  20000, "00-00-1", "inhalation", 30, 1,
  20000, "00-00-2", "inhalation", 40, 1
)
css_df <- tibble::tribble(
  ~casn, ~age_lb, ~age_ub, ~weight, ~css,
  "00-00-1",  0, 49, "Normal", 21,
  "00-00-1", 50, 99, "Normal", 22,
  "00-00-1",  0, 49,  "Obese", 61,
  "00-00-1", 50, 99,  "Obese", 62,
  "00-00-2",  0, 49, "Normal", 11,
  "00-00-2", 50, 99, "Normal", 12,
  "00-00-2",  0, 49,  "Obese", 31,
  "00-00-2", 50, 99,  "Obese", 32
)
hill_df <- tibble::tribble(
  ~assay, ~casn, ~logc, ~resp,
  "a1", "00-00-1",    0,  10,
  "a1", "00-00-1",    1,  20,
  "a1", "00-00-1",    2,  80,
  "a1", "00-00-1",    3, 100,
  "a1", "00-00-2", -0.5,   5,
  "a1", "00-00-2",  0.5,  20,
  "a1", "00-00-2",  1.5,  55,
  "a1", "00-00-2",  2.5,  60
)
GT <- GeoTox() |>
  set_sample(sample_df) |>
  set_simulated_css(css_df) |>
  add_exposure_rate_params() |>
  add_hill_params(fit_hill(hill_df, assay = "assay", substance = "casn")) |>
  simulate_population(exposure = exposure_df)

# Calculate concentrations and risk
GT <- GT |> calc_response()

# Open a connection to GeoTox database
con <- get_con(GT)

# Look at relevant table

dplyr::tbl(con, "concentration") |> dplyr::collect()

dplyr::tbl(con, "risk") |> dplyr::collect()

# Clean up example
DBI::dbDisconnect(con)
file.remove(GT$db_info$dbdir)

---

Calculate risk scores

Description

Calculate generalized concentration addition (GCA) and independent action (IA) risk scores and hazard quotients (HQ) based on in vitro concentration data and Hill parameters.

Usage

calc_risk(
  GT,
  max_mult = 1.5,
  fixed = FALSE,
  overwrite = FALSE,
  risk_name = "risk"
)

Arguments

GT	GeoTox object.
max_mult	Upper bound multiplier for max response (default 1.5).
fixed	Logical indicating whether to set standard deviation parameters of Hill fit to zero (default FALSE). Used in sensitivity analysis.
overwrite	Logical indicating whether to overwrite existing risk table (default FALSE).
risk_name	Table name to store risk results (default "risk"). Values other than "risk" are used in sensitivity analysis.

Details

This function requires that the 'concentration', 'sample', and 'hill_params' tables are present in the GeoTox object. Typically these tables will have been created by prior calls to calc_invitro_concentration() and add_hill_params().

The risk scores are calculated for each sample and assay combination and stored in the 'risk' table in the GeoTox object, unless a different risk_name is provided. Supplying a different risk_name shouldn't be done directly by the user, but rather by calling calc_sensitivity().

Value

The same GeoTox object, invisibly.

See Also

add_hill_params(), calc_invitro_concentration(), calc_response()

Examples

# Setup required tables
sample_df <- tibble::tribble(
  ~FIPS, ~age, ~weight,
  10000, 25, "Normal",
  10000, 35,  "Obese",
  20000, 50, "Normal"
)
exposure_df <- tibble::tribble(
  ~FIPS, ~casn, ~route, ~mean, ~sd,
  10000, "00-00-1", "inhalation", 10, 1,
  10000, "00-00-2", "inhalation", 20, 1,
  20000, "00-00-1", "inhalation", 30, 1,
  20000, "00-00-2", "inhalation", 40, 1
)
css_df <- tibble::tribble(
  ~casn, ~age_lb, ~age_ub, ~weight, ~css,
  "00-00-1",  0, 49, "Normal", 21,
  "00-00-1", 50, 99, "Normal", 22,
  "00-00-1",  0, 49,  "Obese", 61,
  "00-00-1", 50, 99,  "Obese", 62,
  "00-00-2",  0, 49, "Normal", 11,
  "00-00-2", 50, 99, "Normal", 12,
  "00-00-2",  0, 49,  "Obese", 31,
  "00-00-2", 50, 99,  "Obese", 32
)
hill_df <- tibble::tribble(
  ~assay, ~casn, ~logc, ~resp,
  "a1", "00-00-1",    0,  10,
  "a1", "00-00-1",    1,  20,
  "a1", "00-00-1",    2,  80,
  "a1", "00-00-1",    3, 100,
  "a1", "00-00-2", -0.5,   5,
  "a1", "00-00-2",  0.5,  20,
  "a1", "00-00-2",  1.5,  55,
  "a1", "00-00-2",  2.5,  60
)
GT <- GeoTox() |>
  set_sample(sample_df) |>
  set_simulated_css(css_df) |>
  add_exposure_rate_params() |>
  add_hill_params(fit_hill(hill_df, assay = "assay", substance = "casn")) |>
  simulate_population(exposure = exposure_df) |>
  calc_internal_dose() |>
  calc_invitro_concentration()

# Calculate risk
GT <- GT |> calc_risk()

# Open a connection to GeoTox database
con <- get_con(GT)

# Look at relevant table
dplyr::tbl(con, "risk") |> dplyr::collect()

# Clean up example
DBI::dbDisconnect(con)
file.remove(GT$db_info$dbdir)

---

Calculate risk sensitivity to a single variable

Description

Compute risk by varying one variable while holding others fixed.

Usage

calc_sensitivity(
  GT,
  vary = c("age", "weight", "css_params", "fit_params", "C_ext"),
  overwrite = FALSE,
  rate_extra_cols = NULL,
  expos_mean = NULL,
  expos_sd = NULL,
  max_mult = 1.5
)

Arguments

GT	GeoTox object.
vary	Variable to vary. One of "age", "weight", "css_params", "fit_params", or "C_ext".
overwrite	Logical indicating whether to overwrite existing sensitivity analysis results in the GeoTox database.
rate_extra_cols	Additional columns to match from the 'exposure_rate_params' table (default NULL).
expos_mean	Column name of exposure concentration mean in the 'exposure' table (default "mean").
expos_sd	Column name of exposure concentration standard deviation in the 'exposure' table (default "sd").
max_mult	Upper bound multiplier for max response (default 1.5).

Details

The sensitivity analysis makes use of the C$_{ss}$ values stored in the 'fixed_css' table of the GeoTox database. These values are determined using the pre-simulated C$_{ss}$ values supplied to set_simulated_css() and can be set using set_fixed_css() prior to running this function. This step is automatically done when using simulate_population() with sample_css = TRUE.

There are five options for the vary argument:

age

C$_{ss}$ values from the 'age' column of the 'fixed_css' table are used. For other cases, exposure rates are re-simulated using the median age by location in the 'sample' table by calling simulate_exposure_rate() with sensitivity = TRUE.

weight

C$_{ss}$ values from the 'weight' column of the 'fixed_css' table are used.

css_params

C$_{ss}$ values from the 'params' column of the 'fixed_css' table are used.

fit_params

C$_{ss}$ values from the 'other' column of the 'fixed_css' table are used. For other cases, the standard deviation of dose-response model fit parameters are set to zero by calling calc_risk() with fixed = TRUE.

C_ext

C$_{ss}$ values from the 'other' column of the 'fixed_css' table are used. For other cases, external concentrations are re-simulated with standard deviations set to zero by calling simulate_exposure() with sensitivity = TRUE.

In all cases above, the resulting risk table is named 'risk_sensitivity_~vary~' (e.g., 'risk_sensitivity_age') in the GeoTox database.

Inputs rate_extra_cols, expos_mean, and expos_sd do not need to be specified again if they were already provided in a previous call and are set in the GeoTox parameters (GT$par).

Value

The updated GeoTox object, invisibly.

See Also

set_simulated_css(), set_fixed_css(), sensitivity_analysis()

Examples

# Example setup is shown below in \dontrun().
# Pre-generated results will be loaded instead to avoid long example runtime.

## Not run: 
# Setup required tables
sample_df <- tibble::tribble(
  ~FIPS, ~age, ~weight,
  10000, 25, "Normal",
  10000, 35,  "Obese",
  20000, 50, "Normal"
)
exposure_df <- tibble::tribble(
  ~FIPS, ~casn, ~route, ~mean, ~sd,
  10000, "00-00-1", "inhalation", 10, 1,
  10000, "00-00-2", "inhalation", 20, 1,
  20000, "00-00-1", "inhalation", 30, 1,
  20000, "00-00-2", "inhalation", 40, 1
)
css_df <- tibble::tribble(
  ~casn, ~age_lb, ~age_ub, ~weight, ~css,
  "00-00-1",  0, 49, "Normal", 21,
  "00-00-1", 50, 99, "Normal", 22,
  "00-00-1",  0, 49,  "Obese", 61,
  "00-00-1", 50, 99,  "Obese", 62,
  "00-00-2",  0, 49, "Normal", 11,
  "00-00-2", 50, 99, "Normal", 12,
  "00-00-2",  0, 49,  "Obese", 31,
  "00-00-2", 50, 99,  "Obese", 32
)
hill_df <- tibble::tribble(
  ~assay, ~model, ~casn, ~logc, ~resp,
  "a1", "human", "00-00-1",    0,  10,
  "a1", "human", "00-00-1",    1,  20,
  "a1", "human", "00-00-1",    2,  80,
  "a1", "human", "00-00-1",    3, 100,
  "a1", "human", "00-00-2", -0.5,   5,
  "a1", "human", "00-00-2",  0.5,  20,
  "a1", "human", "00-00-2",  1.5,  55,
  "a1", "human", "00-00-2",  2.5,  60,
  "a2",   "rat", "00-00-1",   -1,   0,
  "a2",   "rat", "00-00-1",    0,  10,
  "a2",   "rat", "00-00-1",    1,  30,
  "a2",   "rat", "00-00-1",    2,  40
)
set.seed(1234)
GT <- GeoTox() |>
  set_sample(sample_df) |>
  set_simulated_css(css_df) |>
  add_exposure_rate_params() |>
  add_hill_params(fit_hill(
    hill_df, assay = c(name = "assay", model = "model"), substance = "casn"
  )) |>
  simulate_population(exposure = exposure_df) |>
  calc_response()

# Calculate sensitivity to age
GT <- GT |> calc_sensitivity("age")

## End(Not run)

# Load results from pre-generated database for this example
temp_dir <- tempdir()
zip::unzip(
  system.file("extdata", "sensitivity.duckdb.zip", package = "GeoTox"),
  junkpaths = TRUE,
  exdir = temp_dir
)
GT <- GeoTox(paste0(temp_dir, "/sensitivity.duckdb"))

# Open a connection to GeoTox database
con <- get_con(GT)

# Look at relevant table

dplyr::tbl(con, "risk_sensitivity_age") |> dplyr::collect()

# Compared to baseline risk table
dplyr::tbl(con, "risk") |> dplyr::collect()

# Clean up example
DBI::dbDisconnect(con)
file.remove(GT$db_info$dbdir)

---

Fit 2- or 3-parameter Hill model

Description

Fit a 2-parameter (fixed slope) or 3-parameter (variable slope) Hill model to concentration-response data.

Usage

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

Arguments

x	Data frame of dose response data.
conc	Column name of base-10 log scaled concentration (default "logc").
resp	Column name of response (default "resp").
fixed_slope	Logical indicating whether to fit a 2-parameter (TRUE) or 3-parameter (FALSE) Hill model (default TRUE).
assay	Column name of assay identifier(s) (optional, default NULL).
substance	Column name of substance identifier(s) (optional, default NULL).

Details

The input x data frame must contain columns specified by conc and resp arguments representing the log10-transformed concentration and response, respectively.

Optional assay and substance identifiers can be named vectors that are used to fit multiple substances and/or assays. For example, assay = c(name = "assay", "model") would indicate that x contains both and assay name and model. The name = "assay" part would rename the "assay" column in x to "name" in the 'assay' table when aded with add_hill_params().

Returned column '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

A list with elements 'fit', 'assay', 'substance'. The 'fit' element is a data frame of fit parameters, while the 'assay' and 'substance' elements indicate the column names used for assay and substance identifiers, respectively.

See Also

add_hill_params()

Examples

hill_df <- tibble::tribble(
  ~assay, ~model, ~casn, ~logc, ~resp,
  "a1", "human", "00-00-1",    0,  10,
  "a1", "human", "00-00-1",    1,  20,
  "a1", "human", "00-00-1",    2,  80,
  "a1", "human", "00-00-1",    3, 100,
  "a1", "human", "00-00-2", -0.5,   5,
  "a1", "human", "00-00-2",  0.5,  20,
  "a1", "human", "00-00-2",  1.5,  55,
  "a1", "human", "00-00-2",  2.5,  60,
  "a2",   "rat", "00-00-1",   -1,   0,
  "a2",   "rat", "00-00-1",    0,  10,
  "a2",   "rat", "00-00-1",    1,  30,
  "a2",   "rat", "00-00-1",    2,  40
)

# Fit 2-parameter Hill model
fit_hill(
  hill_df, assay = c(name = "assay", model = "model"), substance = "casn"
)

# Fit 3-parameter Hill model
fit_hill(hill_df, assay = "assay", substance = "casn", 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

Format

A list with items:

exposure

2020 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 7/1/2020 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 2020.

---

GeoTox S3 object

Description

Create a GeoTox object and connect to the underlying database.

Usage

GeoTox(dbname = tempfile(fileext = ".duckdb"), reset_seed = FALSE, ...)

get_con(GT)

Arguments

dbname	Database file name. Default is a temporary file.
reset_seed	Logical indicating whether to reset the user's global .Random.seed after certain database operations (default FALSE).
...	Additional arguments passed to DBI::dbConnect().
GT	GeoTox object.

Details

The dbname will point to a DuckDB database file. If the file does not already exist, a new database will be created. Additional arguments passed via ... will be forwarded to DBI::dbConnect().

The reset_seed parameter is necessary for replicating results from the previous GeoTox implementation. Some database functions create temporary tables where a random string is appended to the table name. This advances the .Random.seed state and causes functions that use randomness to produce different results between the previous and current implementations. Setting reset_seed = TRUE will reset the user's .Random.seed to the value it had before certain database operations.

Various parameters will be stored in the 'par' table. These parameters will also be loaded into the GeoTox object as a list in GT$par. The value for reset_seed can only be set on initial GeoTox object creation and will be loaded from the 'par' table for existing databases.

Value

For GeoTox(), a GeoTox S3 object. For get_con(), a database connection.

Examples

# Create a GeoTox object
GT <- GeoTox()

# Open a connection to GeoTox database
con <- get_con(GT)

# List database tables
DBI::dbListTables(con)

# Look at the GT parameters
dplyr::tbl(con, "par") |> dplyr::collect()
str(GT$par)

# Clean up example
DBI::dbDisconnect(con)
file.remove(GT$db_info$dbdir)

---

Get risk metric results

Description

Several functions used to fetch risk metric results from risk tables in a GeoTox database. The outputs of these functions are useful for plotting or further analysis.

Usage

get_assay_table(GT)

get_risk_quantiles(
  GT,
  metric = c("GCA.Eff", "IA.Eff", "GCA.HQ.10", "IA.HQ.10"),
  quantiles = c(0.1, 0.25, 0.5, 0.75, 0.9),
  table_name = "risk"
)

get_risk_sensitivity(
  GT,
  metric = c("GCA.Eff", "IA.Eff", "GCA.HQ.10", "IA.HQ.10"),
  assay = NULL
)

get_risk_values(
  GT,
  metric = c("GCA.Eff", "IA.Eff", "GCA.HQ.10", "IA.HQ.10"),
  assay = NULL,
  table_name = "risk"
)

Arguments

GT	GeoTox object.
metric	Risk metric to retrieve. One of "GCA.Eff", "IA.Eff", "GCA.HQ.10", or "IA.HQ.10".
quantiles	Numeric vector of quantiles to calculate.
table_name	Name of the risk table to query (default "risk").
assay	Named vector specifying an assay filter (default NULL).

Details

Normally an 'assay' table is created when adding the Hill model concentration-response fit parameters with add_hill_params(). If no assay input is specified in fit_hill(), then an 'assay' table will not be created. For get_risk_values(), the assay parameter can be used to filter results based on assay details stored in the 'assay' table. This is useful when multiple assays are available in the database. The assay input should be a named vector specifying the column name and value to filter by, e.g. assay = c(endp = "mortality"). For get_risk_quantiles(), if there is no assay data in the GeoTox database, then the output "assay_id" column will be filled with NA values. If there is assay data, use get_assay_table() to retrieve assay information and link the "assay_id" to assay details.

Use the table_name parameter to specify which risk table to query. There can be several risk tables in a GeoTox database. The default table is named "risk" and is created by either calc_risk() or the wrapper function calc_response(). Sensitivity analysis results created by either calc_sensitivity() or the wrapper function sensitivity_analysis() are stored in other tables with names like "risk_sensitivity_age", etc. Refer to calc_sensitivity() to see which tables may be available.

get_risk_sensitivity() is a wrapper function for get_risk_values() around all risk tables created by the original risk computation and subsequent sensitivity analysis. The column names are "baseline" for the original risk table, while the other column names correspond to the parameter varied in the sensitivity analysis.

Value

A data frame or vector.

See Also

calc_risk(), calc_response(), calc_sensitivity(), sensitivity_analysis(), fit_hill(), add_hill_params()

Examples

# Example setup is shown below in \dontrun().
# Pre-generated results will be loaded instead to avoid long example runtime.

## Not run: 
# Setup required tables
sample_df <- tibble::tribble(
  ~FIPS, ~age, ~weight,
  10000, 25, "Normal",
  10000, 35,  "Obese",
  20000, 50, "Normal"
)
exposure_df <- tibble::tribble(
  ~FIPS, ~casn, ~route, ~mean, ~sd,
  10000, "00-00-1", "inhalation", 10, 1,
  10000, "00-00-2", "inhalation", 20, 1,
  20000, "00-00-1", "inhalation", 30, 1,
  20000, "00-00-2", "inhalation", 40, 1
)
css_df <- tibble::tribble(
  ~casn, ~age_lb, ~age_ub, ~weight, ~css,
  "00-00-1",  0, 49, "Normal", 21,
  "00-00-1", 50, 99, "Normal", 22,
  "00-00-1",  0, 49,  "Obese", 61,
  "00-00-1", 50, 99,  "Obese", 62,
  "00-00-2",  0, 49, "Normal", 11,
  "00-00-2", 50, 99, "Normal", 12,
  "00-00-2",  0, 49,  "Obese", 31,
  "00-00-2", 50, 99,  "Obese", 32
)
hill_df <- tibble::tribble(
  ~assay, ~model, ~casn, ~logc, ~resp,
  "a1", "human", "00-00-1",    0,  10,
  "a1", "human", "00-00-1",    1,  20,
  "a1", "human", "00-00-1",    2,  80,
  "a1", "human", "00-00-1",    3, 100,
  "a1", "human", "00-00-2", -0.5,   5,
  "a1", "human", "00-00-2",  0.5,  20,
  "a1", "human", "00-00-2",  1.5,  55,
  "a1", "human", "00-00-2",  2.5,  60,
  "a2",   "rat", "00-00-1",   -1,   0,
  "a2",   "rat", "00-00-1",    0,  10,
  "a2",   "rat", "00-00-1",    1,  30,
  "a2",   "rat", "00-00-1",    2,  40
)
set.seed(1234)
GT <- GeoTox() |>
  set_sample(sample_df) |>
  set_simulated_css(css_df) |>
  add_exposure_rate_params() |>
  add_hill_params(fit_hill(
    hill_df, assay = c(name = "assay", model = "model"), substance = "casn"
  )) |>
  simulate_population(exposure = exposure_df) |>
  calc_response() |>
  sensitivity_analysis()

## End(Not run)

# Load results from pre-generated database for this example
temp_dir <- tempdir()
zip::unzip(
  system.file("extdata", "sensitivity.duckdb.zip", package = "GeoTox"),
  junkpaths = TRUE,
  exdir = temp_dir
)
GT <- GeoTox(paste0(temp_dir, "/sensitivity.duckdb"))

# Look at 'assay' table contents
get_assay_table(GT)

# Get "GCA.HQ.10" values from 'risk' table for the "a1" assay
get_risk_values(GT, metric = "GCA.HQ.10", assay = c(name = "a1"))

# Get "IA.Eff" values from all risk tables for the "a2" assay
get_risk_sensitivity(GT, metric = "IA.Eff", assay = c(name = "a2"))

# Get "GCA.Eff" quantiles from 'risk' table
get_risk_quantiles(GT, metric = "GCA.Eff", quantiles = c(0.25, 0.5, 0.75))

# Open a connection to GeoTox database
con <- get_con(GT)

# Look at the 'risk' table contents
dplyr::tbl(con, "risk") |> dplyr::collect()

# Clean up example
DBI::dbDisconnect(con)
file.remove(GT$db_info$dbdir)

---

Get concentration mean values

Description

Calculates the concentration mean values for each substance and route at each location based on the data in the 'concentration' and 'sample' tables of the GeoTox database. The output of this function is useful for plotting or further analysis.

Usage

get_concentration_mean(GT, col)

Arguments

GT	GeoTox object.
col	Column name in the 'concentration' table for which to calculate the mean, grouped by substance, route, and location.

Details

The col parameter specifies which column in the 'concentration' table to use for the mean calculation. The available choices will depend on what data has been stored in the 'concentration' table during the simulation process.

Value

A data frame.

Examples

# Setup required tables
exposure_df <- tibble::tribble(
  ~FIPS, ~casn, ~route, ~mean, ~sd,
  10000, "00-00-1", "inhalation", 10, 1,
  10000, "00-00-2", "inhalation", 20, 1,
  20000, "00-00-1", "inhalation", 30, 1,
  20000, "00-00-2", "inhalation", 40, 1
)
GT <- GeoTox() |>
  add_exposure(exposure_df) |>
  simulate_exposure(n = 100)

# Calculate mean external concentration by substance and location
get_concentration_mean(GT, "C_ext")

# Open a connection to GeoTox database
con <- get_con(GT)

# Look at column names in the 'concentration' table
dplyr::tbl(con, "concentration") |> dplyr::collect()

# Clean up example
DBI::dbDisconnect(con)
file.remove(GT$db_info$dbdir)

---

Sample from pre-simulated steady-state plasma concentrations

Description

Sample steady-state plasma concentrations (C$_{ss}$) for individuals from pre-simulated values stored in the 'simulated_css' table in a GeoTox database.

Usage

sample_simulated_css(GT, css_extra_cols = NULL, substance_order = NULL)

Arguments

GT	GeoTox object.
css_extra_cols	Additional columns to match from the 'simulated_css' table (default NULL).
substance_order	Named list specifying order of substance evaluation (default NULL).

Details

The C$_{ss}$ values are sampled from the 'simulated_css' table, which must already exist in the GeoTox database; it can be created using set_simulated_css(). The minimum characteristics that are used to match individuals to sets of C$_{ss}$ values are age and weight category ("Normal" or "Obese"). Additional columns (which must exist in both the 'simulated_css' and 'sample' tables) can be specified using the css_extra_cols argument. If css_exta_cols is provided it will be added to the GeoTox object parameter list, GT$par.

In addition to the 'simulated_css' table, both 'sample' and 'concentration' tables must also exist in the GeoTox database. The 'sample' table must contain the characteristics of individuals (age, weight category, and any additional columns specified in css_extra_cols). One way to create this 'sample' table is by using set_sample(). The 'concentration' table will typically be created using simulate_exposure() and will be populated with rows for each individual and substance combination where exposure data is available. The sampled C$_{ss}$ values will be stored in a new "C_ss" column in the 'concentration' table.

The substance_order argument can be used to specify the order in which substances are evaluated when sampling C$_{ss}$ values. The default is to evaluate substances in the order they appear in the 'substance' table. However, if a different order is needed for some reason (e.g., replication of results from a previous GeoTox implementation), the user can provide a named list where the name is the column in the 'substance' table to use for ordering (e.g., "casn") and the value is a vector of substance identifiers in the desired order. If provided, the substance_order will be added to the GeoTox object parameter list, GT$par.

Value

The updated GeoTox object, invisibly.

See Also

set_simulated_css(), simulate_population()

Examples

# Create required tables
sample_df <- tibble::tribble(
  ~FIPS, ~age, ~weight,
  10000, 25, "Normal",
  10000, 35,  "Obese",
  20000, 50, "Normal"
)
exposure_df <- tibble::tribble(
  ~FIPS, ~casn, ~route, ~mean, ~sd,
  10000, "00-00-1", "inhalation", 10, 1,
  10000, "00-00-2", "inhalation", 20, 1,
  20000, "00-00-1", "inhalation", 30, 1,
  20000, "00-00-2", "inhalation", 40, 1
)
# Note: normally the css_df would have many more rows for each combination of
# the non-'css' columns to allow for sampling.
css_df <- tibble::tribble(
  ~casn, ~age_lb, ~age_ub, ~weight, ~css,
  "00-00-1",  0, 49, "Normal",  1,
  "00-00-1", 50, 99, "Normal",  2,
  "00-00-1",  0, 49,  "Obese", 11,
  "00-00-1", 50, 99,  "Obese", 12,
  "00-00-2",  0, 49, "Normal", 21,
  "00-00-2", 50, 99, "Normal", 22,
  "00-00-2",  0, 49,  "Obese", 31,
  "00-00-2", 50, 99,  "Obese", 32
)
GT <- GeoTox() |>
  set_sample(sample_df) |>
  add_exposure(exposure_df) |>
  simulate_exposure() |>
  set_simulated_css(css_df)

# Sample simulated C_ss values
GT <- GT |> sample_simulated_css()

# Open a connection to GeoTox database
con <- get_con(GT)

# Look at created tables. sample_simulated_css() generated the 'C_ss' column
# of the 'concentration' table.

dplyr::tbl(con, "concentration") |> dplyr::collect()

dplyr::tbl(con, "sample") |> dplyr::collect()

dplyr::tbl(con, "location") |> dplyr::collect()

dplyr::tbl(con, "substance") |> dplyr::collect()

dplyr::tbl(con, "route") |> dplyr::collect()

# Replace sample and css tables with new data including an extra column
# Limit to a single substance for simplicity
sample_df <- tibble::tribble(
  ~FIPS, ~age, ~weight, ~sign,
  10000, 25, "Normal", "+",
  10000, 35,  "Obese", "-",
  20000, 50, "Normal", "+"
)
css_df <- tibble::tribble(
  ~casn, ~age_lb, ~age_ub, ~weight, ~css, ~sign,
  "00-00-1",  0, 49, "Normal",   1, "+",
  "00-00-1", 50, 99, "Normal",   2, "+",
  "00-00-1",  0, 49,  "Obese",  11, "+",
  "00-00-1", 50, 99,  "Obese",  12, "+",
  "00-00-1",  0, 49, "Normal",  -1, "-",
  "00-00-1", 50, 99, "Normal",  -2, "-",
  "00-00-1",  0, 49,  "Obese", -11, "-",
  "00-00-1", 50, 99,  "Obese", -12, "-"
)
GT <- GT |>
 set_sample(sample_df, overwrite = TRUE) |>
 simulate_exposure() |>
 set_simulated_css(css_df, overwrite = TRUE)

# Sample simulated C_ss values with extra column
# Notice how the extra column name is added to GT$par
str(GT$par)
GT <- GT |> sample_simulated_css(css_extra_cols = "sign")
str(GT$par)

# Look at new 'concentration' table. Values will be missing for substance 2
# since it is not in the new css_df.
dplyr::tbl(con, "concentration") |> dplyr::collect()

# Clean up example
DBI::dbDisconnect(con)
file.remove(GT$db_info$dbdir)

---

Perform sensitivity analysis

Description

Calculate risk sensitivity to all available vary parameters in calc_sensitivity().

Usage

sensitivity_analysis(GT, max_mult = c(1.5, 1.5, 1.5, 1.5, 1.5), ...)

Arguments

GT	GeoTox object.
max_mult	Vector of length 5 containing upper bound multipliers for max response (default 1.5).
...	Additional arguments passed to each call of calc_sensitivity().

Details

Sensitivity is calculated in the order: age, weight, css_params, fit_params, C_ext. The max_mult vector allows specifying different upper bound multipliers for each parameter.

Value

The updated GeoTox object, invisibly.

See Also

calc_sensitivity()

Examples

# Example setup is shown below in \dontrun().
# Pre-generated results will be loaded instead to avoid long example runtime.

## Not run: 
# Setup required tables
sample_df <- tibble::tribble(
  ~FIPS, ~age, ~weight,
  10000, 25, "Normal",
  10000, 35,  "Obese",
  20000, 50, "Normal"
)
exposure_df <- tibble::tribble(
  ~FIPS, ~casn, ~route, ~mean, ~sd,
  10000, "00-00-1", "inhalation", 10, 1,
  10000, "00-00-2", "inhalation", 20, 1,
  20000, "00-00-1", "inhalation", 30, 1,
  20000, "00-00-2", "inhalation", 40, 1
)
css_df <- tibble::tribble(
  ~casn, ~age_lb, ~age_ub, ~weight, ~css,
  "00-00-1",  0, 49, "Normal", 21,
  "00-00-1", 50, 99, "Normal", 22,
  "00-00-1",  0, 49,  "Obese", 61,
  "00-00-1", 50, 99,  "Obese", 62,
  "00-00-2",  0, 49, "Normal", 11,
  "00-00-2", 50, 99, "Normal", 12,
  "00-00-2",  0, 49,  "Obese", 31,
  "00-00-2", 50, 99,  "Obese", 32
)
hill_df <- tibble::tribble(
  ~assay, ~model, ~casn, ~logc, ~resp,
  "a1", "human", "00-00-1",    0,  10,
  "a1", "human", "00-00-1",    1,  20,
  "a1", "human", "00-00-1",    2,  80,
  "a1", "human", "00-00-1",    3, 100,
  "a1", "human", "00-00-2", -0.5,   5,
  "a1", "human", "00-00-2",  0.5,  20,
  "a1", "human", "00-00-2",  1.5,  55,
  "a1", "human", "00-00-2",  2.5,  60,
  "a2",   "rat", "00-00-1",   -1,   0,
  "a2",   "rat", "00-00-1",    0,  10,
  "a2",   "rat", "00-00-1",    1,  30,
  "a2",   "rat", "00-00-1",    2,  40
)
set.seed(1234)
GT <- GeoTox() |>
  set_sample(sample_df) |>
  set_simulated_css(css_df) |>
  add_exposure_rate_params() |>
  add_hill_params(fit_hill(
    hill_df, assay = c(name = "assay", model = "model"), substance = "casn"
  )) |>
  simulate_population(exposure = exposure_df) |>
  calc_response()

# Perform sensitivity analysis
GT <- GT |> sensitivity_analysis()

## End(Not run)

# Load results from pre-generated database for this example
temp_dir <- tempdir()
zip::unzip(
  system.file("extdata", "sensitivity.duckdb.zip", package = "GeoTox"),
  junkpaths = TRUE,
  exdir = temp_dir
)
GT <- GeoTox(paste0(temp_dir, "/sensitivity.duckdb"))

# Open a connection to GeoTox database
con <- get_con(GT)

# Look at relevant table

dplyr::tbl(con, "risk_sensitivity_age") |> dplyr::collect()

dplyr::tbl(con, "risk_sensitivity_weight") |> dplyr::collect()

dplyr::tbl(con, "risk_sensitivity_css_params") |> dplyr::collect()

dplyr::tbl(con, "risk_sensitivity_fit_params") |> dplyr::collect()

dplyr::tbl(con, "risk_sensitivity_C_ext") |> dplyr::collect()

# Compared to baseline risk table
dplyr::tbl(con, "risk") |> dplyr::collect()

# Clean up example
DBI::dbDisconnect(con)
file.remove(GT$db_info$dbdir)

---

Store and retrieve boundary geometries

Description

Boundary information is stored as serialized 'BLOB' objects in the 'boundary' table.

Usage

set_boundary(GT, df_list, location = "county", overwrite = FALSE)

get_boundary(GT)

Arguments

GT	GeoTox object.
df_list	Named list of data frames containing boundary geometries as sf objects.
location	Name of element in df_list that contains location boundary information (default "county").
overwrite	Logical indicating whether to overwrite existing 'boundary' table.

Details

NOTE: This function requires the sf package to be installed.

This function takes a named list of sf objects and stores them in the database. If a location boundary (default "county") is provided, then the non-geometry fields will be added to the 'location' table and replaced with a 'location_id' value so they can be linked to data in the 'sample' table.

Value

For set_boundary(), the same GeoTox object, invisibly. For get_boundary(), a data frame with columns 'id' (boundary name) and 'data' (sf object).

Examples

# Setup sf objects
county <- sf::st_sf(
  FIPS = c(10000, 20000),
  geometry = sf::st_sfc(sf::st_point(1:2), sf::st_point(3:4))
)
state <- sf::st_sf(
  STATE = "XYZ",
  geometry = sf::st_sfc(sf::st_point(5:6))
)
df_list <- list(county = county, state = state)

# Add boundary to GeoTox database
GT <- GeoTox() |> set_boundary(df_list)

# Open a connection to GeoTox database
con <- get_con(GT)

# Look at created tables

dplyr::tbl(con, "boundary") |> dplyr::collect()

dplyr::tbl(con, "location") |> dplyr::collect()

# Retrieve boundary from GeoTox database
boundary <- get_boundary(GT)
boundary
boundary |> tibble::deframe()

# Clean up example
DBI::dbDisconnect(con)
file.remove(GT$db_info$dbdir)

---

Prepare steady-state plasma concentrations for sensitivity analysis

Description

Create the 'fixed_css' table in the GeoTox database, which contains values of steady-state plasma concentrations (C$_{ss}$) for sensitivity analysis.

Usage

set_fixed_css(GT, substance_order = NULL)

Arguments

GT	GeoTox object.
substance_order	Named list specifying order of substance evaluation (default NULL).

Details

Several tables are required in the GeoTox database before the 'fixed_css' table can be created. Typically, set_fixed_css() is called after using sample_simulated_css(), at which point all required tables will be present.

The resulting 'fixed_css' table is used for sensitivity analysis where one parameter is allowed to vary at a time while all other parameters are held constant at fixed values.

The substance_order argument can be used to specify the order in which substances are evaluated when sampling C$_{ss}$ values. The default is to evaluate substances in the order they appear in the 'substance' table. However, if a different order is needed for some reason (e.g., replication of results from a previous GeoTox implementation), the user can provide a named list where the name is the column in the 'substance' table to use for ordering (e.g., "casn") and the value is a vector of substance identifiers in the desired order. If provided, the substance_order will be added to the GeoTox object parameter list, GT$par. This is only applicable to the "params" section described below.

Table 'fixed_css' columns:

age

Median pre-simulated C$_{ss}$ values are computed by age group for each substance, then the median C$_{ss}$ values are assigned to each individual based on their age group.

weight

Median pre-simulated C$_{ss}$ values are computed by weight category for each substance, then the median C$_{ss}$ values are assigned to each individual based on their weight category.

params

Individuals are assigned the median age of their location and pre-simulated C$_{ss}$ values for the "Normal" weight category are sampled.

other

Median sampled C$_{ss}$ values are computed across all substances for each location after mean-imputation of missing C$_{ss}$ values for each substance and location. The median C$_{ss}$ values are assigned to each individual based on their location.

Value

The updated GeoTox object, invisibly.

See Also

sample_simulated_css(), simulate_population()

Examples

# Create required tables
sample_df <- tibble::tribble(
  ~FIPS, ~age, ~weight,
  10000, 25, "Normal",
  10000, 35,  "Obese",
  20000, 50, "Normal"
)
exposure_df <- tibble::tribble(
  ~FIPS, ~casn, ~route, ~mean, ~sd,
  10000, "00-00-1", "inhalation", 10, 1,
  10000, "00-00-2", "inhalation", 20, 1,
  20000, "00-00-1", "inhalation", 30, 1,
  20000, "00-00-2", "inhalation", 40, 1
)
# Note: normally the css_df would have many more rows for each combination of
# the non-'css' columns to allow for sampling.
css_df <- tibble::tribble(
  ~casn, ~age_lb, ~age_ub, ~weight, ~css,
  "00-00-1",  0, 49, "Normal",  1,
  "00-00-1", 50, 99, "Normal",  2,
  "00-00-1",  0, 49,  "Obese", 11,
  "00-00-1", 50, 99,  "Obese", 12,
  "00-00-2",  0, 49, "Normal", 21,
  "00-00-2", 50, 99, "Normal", 22,
  "00-00-2",  0, 49,  "Obese", 31,
  "00-00-2", 50, 99,  "Obese", 32
)
GT <- GeoTox() |>
  set_sample(sample_df) |>
  add_exposure(exposure_df) |>
  simulate_exposure() |>
  set_simulated_css(css_df) |>
  sample_simulated_css()

# Set fixed C_ss values
GT <- GT |> set_fixed_css()

# Open a connection to GeoTox database
con <- get_con(GT)

# Look at created tables
# Note: the 'age', 'weight', 'params', and 'other' columns of the
# 'fixed_css' table contain the C_ss values for sensitivity analysis.
# For example, the 'age' column doesn't contain ages, but C_ss values.

dplyr::tbl(con, "fixed_css") |> dplyr::collect()

dplyr::tbl(con, "concentration") |> dplyr::collect()

dplyr::tbl(con, "sample") |> dplyr::collect()

dplyr::tbl(con, "location") |> dplyr::collect()

dplyr::tbl(con, "substance") |> dplyr::collect()

# Clean up example
DBI::dbDisconnect(con)
file.remove(GT$db_info$dbdir)

---

Set sample data

Description

Create the 'sample' table in the GeoTox database.

Usage

set_sample(GT, df, location = "FIPS", overwrite = FALSE)

Arguments

GT	GeoTox object.
df	Data frame containing sample data.
location	Column name(s) in df that contain location identifier(s) (default "FIPS").
overwrite	Logical indicating whether to overwrite existing 'sample' table and remove existing 'concentration' and 'risk' tables (default FALSE).

Details

The 'sample' table consists of individual characteristics and location information. At a minimum, it must contain columns with information on age, weight category, and location identifier(s).

When the df input contains information on age or weight category, the column names must be "age" and "weight", respectively. The location input (default "FIPS") can be a named vector to specify multiple identifier columns in df. For example, location = c(FIPS = "FIPS", state = "ST") would indicate that df contains both FIPS codes and state identifiers for locations. The location column(s) will be added to the 'location' table and replaced with a corresponding "location_id" column in the 'sample' table. The state = "ST" part in the example above would rename the "ST" column in df to "state" in the 'location' table.

There are several other functions that can be used to create or add to the 'sample' table: simulate_age() to generate age data, simulate_obesity() to generate weight category data, and simulate_population(), which is a wrapper function that can generate both age and weight category data along with other fields. set_sample() can be used in combination with these functions to first set some known sample data, e.g. age, and then simulate any missing fields, e.g. weight category.

If overwrite = TRUE, any existing 'concentration' and 'risk' tables will be dropped before creating the 'sample' table. This is because the existing concentration and risk data would no longer be valid for the new sample data. Further downstream tables are not dropped automatically, but can be updated during subsequent simulation and analysis steps.

Value

The same GeoTox object, invisibly.

See Also

simulate_age(), simulate_obesity(), simulate_population()

Examples

# Example sample data
sample_df <- tibble::tribble(
  ~FIPS, ~age, ~weight,
  10000, 25, "Normal",
  10000, 35,  "Obese",
  20000, 50, "Normal"
)

# Set sample data
GT <- GeoTox() |> set_sample(sample_df)

# Open a connection to GeoTox database
con <- get_con(GT)

# Look at created tables

dplyr::tbl(con, "sample") |> dplyr::collect()

dplyr::tbl(con, "location") |> dplyr::collect()

# Clean up example
DBI::dbDisconnect(con)
file.remove(GT$db_info$dbdir)

---

Set pre-simulated steady-state plasma concentrations

Description

Create the 'simulated_css' table in a GeoTox database, which contains pre-simulated steady-state plasma concentrations (C$_{ss}$).

Usage

set_simulated_css(GT, df, substance = "casn", overwrite = FALSE)

Arguments

GT	GeoTox object.
df	Data frame containing simulated C$_{ss}$ values for groups of population characteristics.
substance	Column name(s) in df that contain substance identifier(s) (default "casn").
overwrite	Logical indicating whether to overwrite existing 'simulated_css' table (default FALSE).

Details

The 'simulated_css' table is used by sample_simulated_css() to assign C$_{ss}$ values to individuals. The minimum required columns in the df data frame are "age_lb", "age_ub", "weight", "css", and at least one column with substance information (default "casn").

The values for "age_lb" and "age_ub" should be non-overlapping integers representing age ranges (in years). For example, two subsequent age groups might be c(0, 4) and c(5, 9). The "weight" column should contain the weight category and contain values of either "Normal" or "Obese". The "css" column should contain the pre-simulated C$_{ss}$ values.

The substance input can be a named vector to specify multiple substance identifier columns in df. For example, c(casn = "casn", name = "chnm") would indicate that df contains both CAS numbers and chemical names for substances. The name = "chnm" part would rename the "chnm" column in df to "name" in the 'substance' table.

Value

The same GeoTox object, invisibly.

See Also

sample_simulated_css()

Examples

# Example pre-simulated C_ss data
# Note: normally the css_df would have many more rows for each combination of
# the non-'css' columns to allow for sampling.
css_df <- tibble::tribble(
  ~casn, ~age_lb, ~age_ub, ~weight, ~css,
  "00-00-1",  0, 49, "Normal",  1,
  "00-00-1", 50, 99, "Normal",  2,
  "00-00-1",  0, 49,  "Obese", 11,
  "00-00-1", 50, 99,  "Obese", 12,
  "00-00-2",  0, 49, "Normal", 21,
  "00-00-2", 50, 99, "Normal", 22,
  "00-00-2",  0, 49,  "Obese", 31,
  "00-00-2", 50, 99,  "Obese", 32
)

# Set simulated C_ss values
GT <- GeoTox() |> set_simulated_css(css_df)

# Open a connection to GeoTox database
con <- get_con(GT)

# Look at relevant tables

dplyr::tbl(con, "simulated_css") |> dplyr::collect()

dplyr::tbl(con, "substance") |> dplyr::collect()

# Clean up example
DBI::dbDisconnect(con)
file.remove(GT$db_info$dbdir)

---

Simulate age values

Description

Simulate 'age' values to be stored in the 'sample' table of a GeoTox database.

Usage

simulate_age(GT, n = 1000, overwrite = FALSE)

Arguments

GT	GeoTox object.
n	Number of individuals to simulate per location (default 1000). Ignored if 'sample' table already exists.
overwrite	Logical indicating whether to overwrite existing 'age' values in the 'sample' table (default FALSE).

Details

An 'age' table containing simulation data must already exist in the GeoTox database, which is added using add_age().

Value

The same GeoTox object, invisibly.

See Also

add_age(), simulate_population()

Examples

# Example age simulation data
age_df <- data.frame(
  FIPS = rep(c(10000, 20000), each = 19),
  AGEGRP = rep(0:18, times = 2),
  TOT_POP = 0
)
# FIPS 10000, populate age group 40-44
age_df$TOT_POP[c(1, 10)] = 100
# FIPS 20000, populate age groups 50-59
age_df$TOT_POP[c(1, 12, 13) + 19] = c(200, 100, 100)

# Simulate age values
GT <- GeoTox() |>
  add_age(age_df) |>
  simulate_age(n = 5)

# Open a connection to GeoTox database
con <- get_con(GT)

# Look at created tables

dplyr::tbl(con, "sample") |> dplyr::collect()

dplyr::tbl(con, "location") |> dplyr::collect()

# Overwrite existing age values
GT <- GT |> simulate_age(overwrite = TRUE)

# Look at updated 'sample' table
dplyr::tbl(con, "sample") |> dplyr::collect()

# Clean up example
DBI::dbDisconnect(con)
file.remove(GT$db_info$dbdir)

---

Simulate exposure concentrations

Description

Simulate external exposure (C_ext) values to be stored in the 'concentration' table of a GeoTox database.

Usage

simulate_exposure(
  GT,
  n = 1000,
  overwrite = FALSE,
  expos_mean = NULL,
  expos_sd = NULL,
  sensitivity = FALSE
)

Arguments

GT	GeoTox object.
n	Number of individuals to simulate per location (default 1000). Ignored if 'sample' table already exists.
overwrite	Logical indicating whether to overwrite existing 'C_ext' values in the 'concentration' table (default FALSE).
expos_mean	Column name of exposure concentration mean in the 'exposure' table (default "mean").
expos_sd	Column name of exposure concentration standard deviation in the 'exposure' table (default "sd").
sensitivity	Logical indicating whether to simulate exposures for sensitivity analysis (default FALSE).

Details

An 'external' table containing simulation data must already exist in the GeoTox database, which is added using add_exposure().

The inputs expos_mean and expos_sd will be assigned default values of "mean" and "sd", respectively, if not provided and not already set in the GeoTox object's parameters, GT$par. If not NULL, the provided values will also be saved to GT$par.

If sensitivity = TRUE, exposure concentrations will be simulated for sensitivity analysis. Typically this shouldn't be used directly by the user, but rather called by calc_sensitivity(). In this case, the function will use the 'concentration_sensitivity' table instead of the 'concentration' table, and will assume that the 'sample' table already exists.

Value

The updated GeoTox object, invisibly.

See Also

add_exposure(), simulate_population()

Examples

# Example exposure simulation data
exposure_df <- tibble::tribble(
  ~FIPS, ~casn, ~route, ~mean, ~sd,
  10000, "00-00-1", "inhalation", 10, 1,
  10000, "00-00-2", "inhalation", 20, 1,
  20000, "00-00-1", "inhalation", 30, 1,
  20000, "00-00-2", "inhalation", 40, 1
)

# Simulate C_ext values
GT <- GeoTox() |>
  add_exposure(exposure_df) |>
  simulate_exposure(n = 3)

# Open a connection to GeoTox database
con <- get_con(GT)

# Look at created tables

dplyr::tbl(con, "concentration") |> dplyr::collect()

dplyr::tbl(con, "sample") |> dplyr::collect()

dplyr::tbl(con, "location") |> dplyr::collect()

dplyr::tbl(con, "substance") |> dplyr::collect()

dplyr::tbl(con, "route") |> dplyr::collect()

# Replace 'exposure' table with different column names
names(exposure_df)[4:5] <- c("mu", "sigma")
DBI::dbRemoveTable(con, "exposure")
GT |> add_exposure(exposure_df)

# Overwrite 'C_ext' values in existing 'concentration' table
# Must specify new 'exposure' column names
# Notice how the column names are added to GT$par
str(GT$par)
GT <- GT |>
  simulate_exposure(expos_mean = "mu", expos_sd = "sigma", overwrite = TRUE)
str(GT$par)

# Look at updated 'concentration' table
dplyr::tbl(con, "concentration") |> dplyr::collect()

# Clean up example
DBI::dbDisconnect(con)
file.remove(GT$db_info$dbdir)

---

Simulate exposure rates

Description

Simulate exposure rate values for each sample and exposure route based on parameters in the exposure_rate_params table.

Usage

simulate_exposure_rate(
  GT,
  rate_extra_cols = NULL,
  overwrite = FALSE,
  sensitivity = FALSE
)

Arguments

GT	GeoTox object.
rate_extra_cols	Additional columns to match from the 'exposure_rate_params' table (default NULL).
overwrite	Logical indicating whether to overwrite existing 'exposure_rate' table (default FALSE).
sensitivity	Logical indicating whether to simulate exposure rates for sensitivity analysis (default FALSE).

Details

An 'exposure_rate_params' table must already exist in the GeoTox database, which is added using add_exposure_rate_params(). There must also be a 'sample' table with an 'age' column, which can be created using simulate_age() or set_sample(). 'location' and 'route' tables must also exist, but they are created when adding the rate parameters and samples.

If rate_exta_cols is provided it will be added to the GeoTox object parameter list, GT$par. These columns must exist in the 'exposure_rate_params' table and will be used to match between the 'sample' and 'exposure_rate_params' tables when simulating exposure rates.

Typically this function will be called with sensitivity set to FALSE. In this case, the function will create an 'exposure_rate' table with simulated exposure rates for each sample and exposure route.

If sensitivity is TRUE, exposure rates will be simulated for sensitivity analysis. Typically this shouldn't be used directly by the user, but rather called by calc_sensitivity(). In this case, the existing 'exposure_rate' table will be copied to the 'exposure_rate_sensitivity' table where the rate values will be overwritten.

Value

The updated GeoTox object, invisibly.

See Also

add_exposure_rate_params(), simulate_population()

Examples

# Setup required tables
# Note: 'gender' is ignored when using the default rate params
sample_df <- tibble::tribble(
  ~FIPS, ~age, ~weight, ~gender,
  10000, 25, "Normal",   "male",
  10000, 35,  "Obese",   "male",
  20000, 50, "Normal", "female"
)
GT <- GeoTox() |>
  add_exposure_rate_params() |>
  set_sample(sample_df)

# Simulate exposure rates
GT |> simulate_exposure_rate()

# Open a connection to GeoTox database
con <- get_con(GT)

# Look at created tables

dplyr::tbl(con, "exposure_rate") |> dplyr::collect()

dplyr::tbl(con, "sample") |> dplyr::collect()

dplyr::tbl(con, "location") |> dplyr::collect()

dplyr::tbl(con, "route") |> dplyr::collect()

# Replace exposure rate params with a new table that includes gender
params_df <- tibble::tribble(
  ~age_lb, ~age_ub, ~gender, ~mean, ~sd,
   0, 49,   "male", 10, 1,
  50, 99,   "male", 20, 1,
   0, 49, "female", 30, 1,
  50, 99, "female", 40, 1
)
DBI::dbRemoveTable(con, "exposure_rate_params")
GT |> add_exposure_rate_params(params = params_df)

# Overwrite 'rate' values in existing 'exposure_rate' table
# Must specify additional column names
# Notice how the column names are added to GT$par
str(GT$par)
GT <- GT |>
  simulate_exposure_rate(rate_extra_cols = c("gender"), overwrite = TRUE)
str(GT$par)

# Look at updated 'exposure_rate' table
dplyr::tbl(con, "exposure_rate") |> dplyr::collect()

# Clean up example
DBI::dbDisconnect(con)
file.remove(GT$db_info$dbdir)

---

Simulate obesity values

Description

Simulate 'weight' category values to be stored in the 'sample' table of a GeoTox database.

Usage

simulate_obesity(
  GT,
  n = 1000,
  overwrite = FALSE,
  obes_prev = NULL,
  obes_sd = NULL
)

Arguments

GT	GeoTox object.
n	Number of individuals to simulate per location (default 1000). Ignored if 'sample' table already exists.
overwrite	Logical indicating whether to overwrite existing 'weight' values in the 'sample' table (default FALSE).
obes_prev	Column name of obesity prevalence in the 'obesity' table (default "OBESITY_CrudePrev").
obes_sd	Column name of obesity standard deviation in the 'obesity' table (default "OBESITY_SD").

Details

An 'obesity' table containing simulation data must already exist in the GeoTox database, which is added using add_obesity().

The inputs obes_prev and obes_sd will be assigned default values of "OBESITY_CrudePrev" and "OBESITY_SD", respectively, if not provided and not already set in the GeoTox object's parameters, GT$par. If not NULL, the provided values will also be saved to GT$par.

Value

The updated GeoTox object, invisibly.

See Also

add_obesity(), simulate_population()

Examples

# Example obesity simulation data
obesity_df <- data.frame(
  FIPS = c(10000, 20000),
  OBESITY_CrudePrev = c(20, 80),
  OBESITY_SD = 5
)

# Simulate weight category values
GT <- GeoTox() |>
  add_obesity(obesity_df) |>
  simulate_obesity(n = 5)

# Open a connection to GeoTox database
con <- get_con(GT)

# Look at created tables

dplyr::tbl(con, "sample") |> dplyr::collect()

dplyr::tbl(con, "location") |> dplyr::collect()

# Replace 'obesity' table with different column names
names(obesity_df)[2:3] <- c("prev", "sd")
DBI::dbRemoveTable(con, "obesity")
GT |> add_obesity(obesity_df)

# Overwrite 'weight' category values in existing 'sample' table
# Must specify new 'obesity' column names
# Notice how the column names are added to GT$par
str(GT$par)
GT <- GT |>
  simulate_obesity(obes_prev = "prev", obes_sd = "sd", overwrite = TRUE)
str(GT$par)

# Look at updated 'sample' table
dplyr::tbl(con, "sample") |> dplyr::collect()

# Clean up example
DBI::dbDisconnect(con)
file.remove(GT$db_info$dbdir)

---

Simulate population characteristics and exposures

Description

Simulate age, weight category, exposure rates, and external exposures. Sample from pre-simulated steady-state plasma concentrations.

Usage

simulate_population(
  GT,
  age = NULL,
  obesity = NULL,
  exposure = NULL,
  simulate_rate = TRUE,
  sample_css = TRUE,
  ...
)

Arguments

GT	GeoTox object.
age	Data frame with age data.
obesity	Data frame with obesity data.
exposure	Data frame with exposure data.
simulate_rate	Logical indicating whether to simulate exposure rates. This requires that exposure rate parameters have been added using add_exposure_rate_params().
sample_css	Logical indicating whether to sample steady-state plasma concentrations (C$_{ss}$). This requires that a table of simulated C$_{ss}$ values has been set using set_simulated_css(). In addition, set_fixed_css() will be called after sampling to prepare C$_{ss}$ values for sensitivity analysis.
...	Additional arguments passed to wrapped functions (see 'Additional arguments' section of 'Details').

Details

This is a wrapper around several other functions:

• add_age() and simulate_age() for age simulation.

• add_obesity() and simulate_obesity() for weight category simulation.

• add_exposure() and simulate_exposure() for external exposure concentration simulation (C$_{ext}$).

• simulate_exposure_rate() for exposure rate simulation.

• sample_simulated_css() for sampling steady-state plasma concentrations (C$_{ss}$).

• set_fixed_css() to prepare C$_{ss}$ values for sensitivity analysis.

The user can provide data frames for age, obesity, and exposure data; if any of these are provided, the corresponding add and simulate functions will be called. The user can also specify whether to simulate exposure rates and sample C$_{ss}$ values using the simulate_rate and sample_css arguments, respectively. If simulate_rate is TRUE, exposure rate parameters must have been added using add_exposure_rate_params(). If sample_css is TRUE, a table of simulated C$_{ss}$ values must already exist using set_simulated_css().

Additional arguments:

n

Number of samples to simulate (default 1000). Used in simulate_age(), simulate_obesity(), and simulate_exposure(). Ignored if the 'sample' table already exists, in which case the existing sample sizes are used.

location

Column name for location ID (default "FIPS"). Used in add_age(), add_obesity(), and add_exposure().

overwrite

Logical indicating whether to overwrite existing values (default FALSE). Used in simulate_age(), simulate_obesity(), simulate_exposure_rate(), and simulate_exposure().

substance

Column name for substance ID (default "casn"). Used in add_exposure().

route

Column name for exposure route (default "route"). Used in add_exposure().

rate_extra_cols

Additional columns to include in exposure_rate table. Used in simulate_exposure_rate().

obes_prev, obes_sd

Column names for obesity prevalence and standard deviation (default "OBESITY_CrudePrev" and "OBESITY_SD", respectively). Used in simulate_obesity().

expos_mean, expos_sd

Column names for exposure concentration mean and standard deviation (default "mean" and "sd", respectively). Used in simulate_exposure().

css_extra_cols

Additional columns to include in simulated_css table. Used in sample_simulated_css().

substance_order

Named list specifying order of substances. Used in sample_simulated_css() and set_fixed_css().

Value

The updated GeoTox object, invisibly.

See Also

add_age(), simulate_age(), add_obesity(), simulate_obesity(), add_exposure(), simulate_exposure(), simulate_exposure_rate(), sample_simulated_css(), set_fixed_css()

Examples

# Example simulation data

age_df <- data.frame(
  FIPS = rep(c(10000, 20000), each = 19),
  AGEGRP = rep(0:18, times = 2),
  TOT_POP = 0
)
# FIPS 10000, populate age group 40-44
age_df$TOT_POP[c(1, 10)] = 100
# FIPS 20000, populate age groups 50-59
age_df$TOT_POP[c(1, 12, 13) + 19] = c(200, 100, 100)

obesity_df <- data.frame(
  FIPS = c(10000, 20000),
  OBESITY_CrudePrev = c(20, 80),
  OBESITY_SD = 5
)

exposure_df <- tibble::tribble(
  ~FIPS, ~casn, ~route, ~mean, ~sd,
  10000, "00-00-1", "inhalation", 10, 1,
  10000, "00-00-2", "inhalation", 20, 1,
  20000, "00-00-1", "inhalation", 30, 1,
  20000, "00-00-2", "inhalation", 40, 1
)

# Note: normally the css_df would have many more rows for each combination of
# the non-'css' columns to allow for sampling.
css_df <- tibble::tribble(
  ~casn, ~age_lb, ~age_ub, ~weight, ~css,
  "00-00-1",  0, 49, "Normal",  1,
  "00-00-1", 50, 99, "Normal",  2,
  "00-00-1",  0, 49,  "Obese", 11,
  "00-00-1", 50, 99,  "Obese", 12,
  "00-00-2",  0, 49, "Normal", 21,
  "00-00-2", 50, 99, "Normal", 22,
  "00-00-2",  0, 49,  "Obese", 31,
  "00-00-2", 50, 99,  "Obese", 32
)

# Simulate population
GT <- GeoTox() |>
  add_exposure_rate_params() |>
  set_simulated_css(css_df) |>
  simulate_population(
    age = age_df,
    obesity = obesity_df,
    exposure = exposure_df,
    n = 3
  )

# Open a connection to GeoTox database
con <- get_con(GT)

# Look at created tables

dplyr::tbl(con, "concentration") |> dplyr::collect()

dplyr::tbl(con, "sample") |> dplyr::collect()

dplyr::tbl(con, "location") |> dplyr::collect()

dplyr::tbl(con, "substance") |> dplyr::collect()

dplyr::tbl(con, "route") |> dplyr::collect()

# Note: the 'age', 'weight', 'params', and 'other' columns of the
# 'fixed_css' table contain the C_ss values for sensitivity analysis.
# For example, the 'age' column doesn't contain ages, but C_ss values.
dplyr::tbl(con, "fixed_css") |> dplyr::collect()

# Clean up example
DBI::dbDisconnect(con)
file.remove(GT$db_info$dbdir)

---