Random MIC Values/Disk Zones/SIR Generation

These functions can be used for generating random MIC values and disk diffusion diameters, for AMR data analysis practice. By providing a microorganism and antimicrobial drug, the generated results will reflect reality as much as possible.

Usage

random_mic(size = NULL, mo = NULL, ab = NULL, skew = "right",
  severity = 1, ...)

random_disk(size = NULL, mo = NULL, ab = NULL, skew = "left",
  severity = 1, ...)

random_sir(size = NULL, prob_SIR = c(0.33, 0.33, 0.33), ...)

Arguments

size: Desired size of the returned vector. If used in a data.frame call or dplyr verb, will get the current (group) size if left blank.
mo: Any character that can be coerced to a valid microorganism code with as.mo(). Can be the same length as size.
ab: Any character that can be coerced to a valid antimicrobial drug code with as.ab().
skew: Direction of skew for MIC or disk values, either "right" or "left". A left-skewed distribution has the majority of the data on the right.
severity: Skew severity; higher values will increase the skewedness. Default is 2; use 0 to prevent skewedness.
...: Ignored, only in place to allow future extensions.
prob_SIR: A vector of length 3: the probabilities for "S" (1st value), "I" (2nd value) and "R" (3rd value).

Value

class mic for random_mic() (see as.mic()) and class disk for random_disk() (see as.disk())

Details

Internally, MIC and disk zone values are sampled based on clinical breakpoints defined in the clinical_breakpoints data set. To create specific generated values per bug or drug, set the mo and/or ab argument. The MICs are sampled on a log2 scale and disks linearly, using weighted probabilities. The weights are based on the skew and severity arguments:

skew = "right" places more emphasis on lower MIC or higher disk values.
skew = "left" places more emphasis on higher MIC or lower disk values.
severity controls the exponential bias applied.

Examples

random_mic(25)
#> Class 'mic'
#>  [1] 0.016  0.125  0.125  0.25   0.5    0.004  0.002  0.002  0.032  0.004 
#> [11] 0.032  0.0005 0.5    1      0.016  0.008  2      0.125  0.25   0.25  
#> [21] 0.032  1      0.008  0.004  0.25  
random_disk(25)
#> Class 'disk'
#>  [1] 47 20 36 18 44 43 25 28 26 44 36 42  8 28 44 31 29 49 30 28 39 49 25 49 48
random_sir(25)
#> Class 'sir'
#>  [1] R S R S R R S I I I S R R S I I S I I I S S R S S

# add more skewedness, make more realistic by setting a bug and/or drug:
disks <- random_disk(100, severity = 2, mo = "Escherichia coli", ab = "CIP")
plot(disks)

# `plot()` and `ggplot2::autoplot()` allow for coloured bars if `mo` and `ab` are set
plot(disks, mo = "Escherichia coli", ab = "CIP", guideline = "CLSI 2025")


# \donttest{
random_mic(25, "Klebsiella pneumoniae") # range 0.0625-64
#> Class 'mic'
#>  [1] 0.004  4      0.032  16     0.016  16     0.001  0.0002 0.002  0.004 
#> [11] 0.001  0.0005 0.0001 0.002  0.125  >=64   0.004  0.0005 0.001  0.0002
#> [21] 0.0005 0.001  0.0005 0.004  0.032 
random_mic(25, "Klebsiella pneumoniae", "meropenem") # range 0.0625-16
#> Class 'mic'
#>  [1] <=0.5 <=0.5 <=0.5 1     <=0.5 <=0.5 <=0.5 <=0.5 <=0.5 <=0.5 1     1    
#> [13] <=0.5 <=0.5 <=0.5 <=0.5 1     <=0.5 <=0.5 <=0.5 <=0.5 1     1     <=0.5
#> [25] <=0.5
random_mic(25, "Streptococcus pneumoniae", "meropenem") # range 0.0625-4
#> Class 'mic'
#>  [1] >=2 1   1   1   >=2 >=2 >=2 1   >=2 1   1   >=2 1   1   1   >=2 1   1   1  
#> [20] >=2 >=2 1   1   1   1  

random_disk(25, "Klebsiella pneumoniae") # range 8-50
#> Class 'disk'
#>  [1] 28 12 30 26  9 34 30 19 32 31 33 34 30 19 15 24 18 34 12 32 29 33 29 32 17
random_disk(25, "Klebsiella pneumoniae", "ampicillin") # range 11-17
#> Class 'disk'
#>  [1] 18 15 19 13 22 20 22 13 18 14 19 13 12 22 20 21 12 20 18 19 20 22 18 17 20
random_disk(25, "Streptococcus pneumoniae", "ampicillin") # range 12-27
#> Class 'disk'
#>  [1] 21 31 27 28 30 34 16 32 28 25 25 23 29 26 24 28 27 33 30 19 30 22 27 22 32
# }