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]]
#>      100-02-7 101-14-4 101-77-9 106-50-3 119-90-4 120-80-9 123-31-9 133-06-2
#> [1,]   1.0360   0.2379   0.5984  0.06974    8.126  0.05157  0.09568   0.2853
#> [2,]   0.5947   0.1133   1.0650  0.74580    4.563  5.44100  1.33300   2.8310
#> [3,]   0.9630   0.3498   2.3540  4.25700    4.498  2.32100  0.26360   1.9250
#>      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,]  0.07045   2.644  0.4645  0.6918 0.03553  29.760  0.5365   2.727   7.040
#> [2,]  0.38000   3.264  9.1780  1.7800 0.13500  32.090  1.8410   2.797   4.630
#> [3,]  0.03674   2.485  1.3940  1.1850 0.08932   2.286  1.2440   5.798   2.568
#>      91-94-1 92-87-5 95-80-7 95-95-4
#> [1,]  0.4060  0.9832   1.387   59.27
#> [2,]  0.2865  1.1400 148.900   10.36
#> [3,]  0.4039  8.7930   2.325    8.81
#> 

# List inputs
sample_Css(geo_tox_data$simulated_css,
           list(c(34, 29), 55),
           list(c("Obese", "Normal"), "Normal"))
#> [[1]]
#>      100-02-7 101-14-4 101-77-9 106-50-3 119-90-4 120-80-9 123-31-9 133-06-2
#> [1,]    1.471   1.0290   1.2790  1.08700   10.590   3.1300   1.0040    3.583
#> [2,]    1.002   0.2539   0.6829  0.09995    3.419   0.6947   0.4052    2.646
#>      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,]  0.06458  4.9850   3.848   4.336  0.1602   3.687  1.2210  22.360  22.030
#> [2,]  0.03606  0.4679   2.062   2.303  0.1165   1.232  0.5437   5.599   1.669
#>      91-94-1 92-87-5 95-80-7 95-95-4
#> [1,] 0.20150   9.339   5.118 110.000
#> [2,] 0.06294   1.003   1.878   3.353
#> 
#> [[2]]
#>      100-02-7 101-14-4 101-77-9 106-50-3 119-90-4 120-80-9 123-31-9 133-06-2
#> [1,]    1.003   0.2062    1.719   0.4855    3.812   0.3568   0.3265    4.593
#>      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,]  0.04728   1.243   9.162   1.449 0.02623   2.262   2.209   2.472   10.44
#>      91-94-1 92-87-5 95-80-7 95-95-4
#> [1,]  0.0983  0.6278   5.404   3.289
#>