Get risk metric results
Source:R/get_assay_table.R, R/get_risk_quantiles.R, R/get_risk_sensitivity.R, and 1 more
get_risk_values.RdSeveral 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"
)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.
Examples
# Example setup is shown below in \dontrun().
# Pre-generated results will be loaded instead to avoid long example runtime.
if (FALSE) { # \dontrun{
# 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()
} # }
# 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)
#> # A tibble: 2 × 3
#> id name model
#> <int> <chr> <chr>
#> 1 1 a1 human
#> 2 2 a2 rat
# Get "GCA.HQ.10" values from 'risk' table for the "a1" assay
get_risk_values(GT, metric = "GCA.HQ.10", assay = c(name = "a1"))
#> [1] 59.02276 232.12433 190.28473
# Get "IA.Eff" values from all risk tables for the "a2" assay
get_risk_sensitivity(GT, metric = "IA.Eff", assay = c(name = "a2"))
#> # A tibble: 3 × 6
#> C_ext css_params weight age fit_params baseline
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 39.0 38.5 38.8 39.4 39.8 37.1
#> 2 39.2 37.9 39.9 39.6 39.1 38.9
#> 3 40.0 39.9 39.9 40.5 40.3 38.1
# Get "GCA.Eff" quantiles from 'risk' table
get_risk_quantiles(GT, metric = "GCA.Eff", quantiles = c(0.25, 0.5, 0.75))
#> # A tibble: 12 × 4
#> assay_id location_id quantile value
#> <int> <int> <dbl> <dbl>
#> 1 1 1 0.25 62.8
#> 2 1 1 0.5 63.5
#> 3 1 1 0.75 64.3
#> 4 1 2 0.25 68.1
#> 5 1 2 0.5 68.1
#> 6 1 2 0.75 68.1
#> 7 2 1 0.25 37.6
#> 8 2 1 0.5 38.0
#> 9 2 1 0.75 38.4
#> 10 2 2 0.25 38.1
#> 11 2 2 0.5 38.1
#> 12 2 2 0.75 38.1
# Open a connection to GeoTox database
con <- get_con(GT)
# Look at the 'risk' table contents
dplyr::tbl(con, "risk") |> dplyr::collect()
#> # A tibble: 6 × 6
#> assay_id sample_id GCA.Eff IA.Eff GCA.HQ.10 IA.HQ.10
#> <int> <int> <dbl> <dbl> <dbl> <dbl>
#> 1 1 1 62.0 68.2 59.0 59.0
#> 2 1 2 65.0 69.3 232. 232.
#> 3 1 3 68.1 73.1 190. 190.
#> 4 2 1 37.1 37.1 89.1 89.1
#> 5 2 2 38.9 38.9 395. 395.
#> 6 2 3 38.1 38.1 393. 393.
# Clean up example
DBI::dbDisconnect(con)
file.remove(GT$db_info$dbdir)
#> [1] TRUE