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Sample from pre-generated C_ss data

Source: R/sample_Css.R
sample_Css.Rd

Sample from pre-generated C_ss data

Usage

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"))
#> [[1]]
#>      100-02-7 101-14-4 101-77-9 119-90-4 120-80-9 123-31-9 133-06-2 51-28-5
#> [1,]    4.920   0.1287   0.4676    8.464    9.558   0.2806    2.034   73.85
#> [2,]    3.604   0.1479   1.4440   39.100    8.855   0.5412    3.113  663.80
#> [3,]    1.082   0.1107   1.0080    4.670    1.493   0.1780    3.196  263.60
#>      510-15-6 53-96-3 56-38-2 60-11-7 63-25-2 72-43-5 84-74-2 87-86-5 88-06-2
#> [1,]   0.1728   2.383  4.2530  0.6562  0.2141   1.665   1.335  0.5696  0.6158
#> [2,]   0.7114   7.037  3.5020  2.5960  0.8362   1.925   0.481 10.3800  2.4630
#> [3,]   0.1429   8.898  0.4794  4.7950  0.2954   9.999   1.684  0.9618  3.7950
#>      91-94-1 92-87-5 95-80-7 95-95-4
#> [1,]  0.3250  0.4477  17.190   2.716
#> [2,]  1.2530  0.7560   8.034   4.559
#> [3,]  0.3969  0.7545   3.405   3.787
#> 

# List inputs
sample_Css(geo_tox_data$simulated_css,
           list(c(34, 29), 55),
           list(c("Obese", "Normal"), "Normal"))
#> [[1]]
#>      100-02-7 101-14-4 101-77-9 119-90-4 120-80-9 123-31-9 133-06-2 51-28-5
#> [1,]    1.546   1.2190   11.150   17.280    3.063  1.39100   2.3160   37.32
#> [2,]    1.212   0.1619    3.141    6.148    1.150  0.06613   0.3514  130.10
#>      510-15-6 53-96-3 56-38-2 60-11-7 63-25-2 72-43-5 84-74-2 87-86-5 88-06-2
#> [1,]   0.3278   5.419   8.366  16.970  0.7243   7.445   1.058 97.3200   4.430
#> [2,]   0.7411   1.316   6.167   4.806  0.1581   5.345   1.041  0.1131   1.996
#>      91-94-1 92-87-5 95-80-7 95-95-4
#> [1,] 0.09992  0.8279   3.535   2.856
#> [2,] 0.25190  1.4160   1.684  50.310
#> 
#> [[2]]
#>      100-02-7 101-14-4 101-77-9 119-90-4 120-80-9 123-31-9 133-06-2 51-28-5
#> [1,]    1.468   0.1368    1.128    10.93    1.501   0.1941    2.172   622.5
#>      510-15-6 53-96-3 56-38-2 60-11-7 63-25-2 72-43-5 84-74-2 87-86-5 88-06-2
#> [1,]  0.08399   2.352   3.519   1.601  0.2759   8.679   1.494   4.843    4.24
#>      91-94-1 92-87-5 95-80-7 95-95-4
#> [1,]  0.1174   1.366   3.404   2.522
#>