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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]]
#>      100-02-7 101-14-4 101-77-9 106-50-3 119-90-4 120-80-9 123-31-9 133-06-2
#> [1,]   0.4032  0.06119   0.4044  0.04516    3.125    1.505  0.08384   4.1070
#> [2,]   0.8511  0.40070   1.5170  0.20540    8.311    1.713  0.30070   0.8906
#> [3,]   0.6539  0.39460   0.3364  0.68070    1.555    3.633  0.42150   1.9910
#>      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,]  0.08366  0.6665   2.187   2.193 0.03733   3.545   2.067   5.036  0.7018
#> [2,]  0.07002  6.3640   1.945   4.409 0.21210   4.614   1.967   7.638 12.9700
#> [3,]  0.19310  3.7220   1.539   1.727 0.06266   4.898   0.985   9.054  1.5870
#>      91-94-1 92-87-5 95-80-7 95-95-4
#> [1,] 0.06077  0.3466   2.231  11.650
#> [2,] 0.19230  0.8108   1.401  17.550
#> [3,] 0.22590  2.6400   2.103   1.502
#> 

# 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,]   2.1290   0.3820    3.652   0.2247    6.800   0.8395   0.6320    2.746
#> [2,]   0.6427   0.1391    2.175   0.2645    3.766   3.4160   0.4531    1.364
#>      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,]   0.4971   5.819   5.986   3.001 0.06565    4.73  1.1280   1.239   5.524
#> [2,]   0.1654   1.163   1.315   2.156 0.03879  265.30  0.9735   3.735   9.349
#>      91-94-1 92-87-5 95-80-7 95-95-4
#> [1,]  1.3720  1.2370   3.461   9.255
#> [2,]  0.0863  0.3199   4.240   5.488
#> 
#> [[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,]   0.4114   0.2184    1.091   0.4477    3.899   0.7356   0.8564    11.01
#>      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,]  0.09589   4.414   3.463   3.304 0.09706   3.237   1.294   2.108   1.301
#>      91-94-1 92-87-5 95-80-7 95-95-4
#> [1,]  0.1822   2.242   1.932   1.595
#>