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Simulate limit of detection data

Source: R/sim_lod.R
sim_lod.Rd

sim_lod() simulates putting the columns of a given matrix D under a limit of detection (LOD) by calculating the given quantile q of each column and corrupting all values < the quantile to NA, returning the newly corrupted matrix, the binary corruption mask, and a vector of column LODs.

Usage

sim_lod(D, q)

Arguments

D

The input data matrix.

q

A double in the range [0, 1] specifying the quantile to use in creating the column-wise LODs. Passed as the probs argument to the quantile() function.

Value

A list containing:

  • D_tilde: The original matrix D corrupted with < LOD NA values.

  • tilde_mask: A binary matrix of dim(D) specifying the locations of corrupted entries (1) and uncorrupted entries (0).

  • lod: A vector with length(lod) == ncol(D) providing the simulated LOD values corresponding to each column in the D_tilde.

See also

sim_na(), impute_matrix(), sim_data()

Examples

D <- sim_data(5, 5, sigma = 0.8)$D
D
#>            [,1]      [,2]      [,3]      [,4]        [,5]
#> [1,]  2.9302635 0.8938987 0.7291769 0.8853122  0.06966197
#> [2,]  1.4584989 1.0828959 1.3333332 1.3004826  1.15159124
#> [3,] -1.6146424 0.9188127 0.6471552 1.2856310  0.42002327
#> [4,] -0.4990438 1.9652499 1.3747700 1.0188214 -0.67200285
#> [5,]  2.4033674 2.6339801 0.7118914 0.9998089  2.31177279
sim_lod(D, q = 0.2)
#> $D_tilde
#>            [,1]      [,2]      [,3]      [,4]       [,5]
#> [1,]  2.9302635        NA 0.7291769        NA 0.06966197
#> [2,]  1.4584989 1.0828959 1.3333332 1.3004826 1.15159124
#> [3,]         NA 0.9188127        NA 1.2856310 0.42002327
#> [4,] -0.4990438 1.9652499 1.3747700 1.0188214         NA
#> [5,]  2.4033674 2.6339801 0.7118914 0.9998089 2.31177279
#> 
#> $tilde_mask
#>      [,1] [,2] [,3] [,4] [,5]
#> [1,]    0    1    0    1    0
#> [2,]    0    0    0    0    0
#> [3,]    1    0    1    0    0
#> [4,]    0    0    0    0    1
#> [5,]    0    0    0    0    0
#> 
#> $lod
#> [1] -0.72216355  0.91382991  0.69894415  0.97690956 -0.07867099
#>