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] 0.125    <=0.0001 0.004    0.004    0.5      0.032    1        0.008   
#>  [9] 128      0.0002   0.002    0.002    32       0.032    0.0005   0.0002  
#> [17] 0.008    0.001    0.0002   <=0.0001 0.064    0.016    0.008    4       
#> [25] 1       
random_disk(25)
#> Class 'disk'
#>  [1] 50 21 48 47 46 38 45 47 24 28 45 43 19 23 44 19 31 22 40 39 39 36 35 14 34
random_sir(25)
#> Class 'sir'
#>  [1] I S S R I S R I R I S S S R R R I I R R I S R R 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] 0.0001 0.0002 0.016  0.5    0.008  >=8    0.125  0.0002 0.002  0.0005
#> [11] 1      0.0001 0.25   0.0002 0.25   2      0.0005 0.064  0.5    0.004 
#> [21] 0.001  0.064  0.032  0.0005 0.125 
random_mic(25, "Klebsiella pneumoniae", "meropenem") # range 0.0625-16
#> Class 'mic'
#>  [1] <=2 <=2 4   <=2 <=2 4   <=2 4   <=2 4   <=2 <=2 <=2 <=2 <=2 <=2 4   4   <=2
#> [20] 4   <=2 <=2 <=2 <=2 <=2
random_mic(25, "Streptococcus pneumoniae", "meropenem") # range 0.0625-4
#> Class 'mic'
#>  [1] 2       <=0.064 1       <=0.064 <=0.064 2       <=0.064 0.25    0.25   
#> [10] 2       <=0.064 <=0.064 <=0.064 <=0.064 0.25    0.5     0.5     0.25   
#> [19] 0.125   0.125   4       0.25    1       <=0.064 <=0.064

random_disk(25, "Klebsiella pneumoniae") # range 8-50
#> Class 'disk'
#>  [1] 14 19 19 33 33 26 16 27 29 33 24 17 17 15 16 26 26 14 17 29 25 25 24 32 32
random_disk(25, "Klebsiella pneumoniae", "ampicillin") # range 11-17
#> Class 'disk'
#>  [1] 22 15 21 11 19 20 11 14 12 12 17 16 12 10 15 22 19 21 19 17 18 19 21 17 21
random_disk(25, "Streptococcus pneumoniae", "ampicillin") # range 12-27
#> Class 'disk'
#>  [1] 21 25 16 29 27 24 31 22 35 28 26 32 24 18 35 33 24 28 33 25 35 25 32 24 26
# }

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