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] 0.25   0.064  0.5    0.004  0.0001 0.032  0.008  16     0.016  0.125 
#> [11] 0.0002 0.001  0.008  0.004  0.064  256    0.5    32     512    0.0002
#> [21] 0.004  1      0.002  0.0005 0.064 
random_disk(25)
#> Class <disk>
#>  [1] 49 44 31 11 27 33 39 24 19 43 30 44 33 31 22 13 37 19 26 29 44 12 46 18 27
random_sir(25)
#> Class <sir>
#>  [1] I R I S R S R R I R R I S S S I R R S I R S R I R

# 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  0.0002 0.008  0.25   0.0005 0.0002 8      0.032  0.0005 0.016 
#> [11] 0.032  32     1      0.0005 0.016  0.002  0.016  0.0005 0.004  0.25  
#> [21] 0.0005 0.016  0.001  0.25   0.032 
random_mic(25, "Klebsiella pneumoniae", "meropenem") # range 0.0625-16
#> Class <mic>
#>  [1] 0.5 >=1 0.5 0.5 0.5 0.5 >=1 0.5 0.5 0.5 0.5 0.5 0.5 0.5 >=1 0.5 >=1 0.5 0.5
#> [20] 0.5 0.5 >=1 >=1 >=1 >=1
random_mic(25, "Streptococcus pneumoniae", "meropenem") # range 0.0625-4
#> Class <mic>
#>  [1] 0.25    <=0.125 <=0.125 0.25    0.25    <=0.125 0.25    0.25    0.25   
#> [10] 0.25    <=0.125 <=0.125 0.25    0.25    <=0.125 <=0.125 <=0.125 0.25   
#> [19] <=0.125 <=0.125 <=0.125 <=0.125 0.25    0.25    0.25   

random_disk(25, "Klebsiella pneumoniae") # range 8-50
#> Class <disk>
#>  [1] 10 32 34 18 17 31 31 21 18 19 27 18 20 11 34 30 28 20 29  9 21 33 34 25 17
random_disk(25, "Klebsiella pneumoniae", "ampicillin") # range 11-17
#> Class <disk>
#>  [1] 17 17 20 22 13 21 21 14 11 18 18 18 21 18 14 22 19 20 18 16 17 12 22 21 19
random_disk(25, "Streptococcus pneumoniae", "ampicillin") # range 12-27
#> Class <disk>
#>  [1] 24 31 26 26 33 16 29 17 28 27 27 35 33 33 29 32 18 33 33 29 28 32 25 17 31
# }

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