This transforms vectors to a new class mic, which treats the input as decimal numbers, while maintaining operators (such as ">=") and only allowing valid MIC values known to the field of (medical) microbiology.

as.mic(x, na.rm = FALSE)



# S3 method for mic
droplevels(x, as.mic = FALSE, ...)



a character or numeric vector


a logical indicating whether missing values should be removed


a logical to indicate whether the <mic> class should be kept, defaults to FALSE


arguments passed on to methods


Ordered factor with additional class mic, that in mathematical operations acts as decimal numbers. Bare in mind that the outcome of any mathematical operation on MICs will return a numeric value.


To interpret MIC values as RSI values, use as.rsi() on MIC values. It supports guidelines from EUCAST (2011-2022) and CLSI (2011-2022).

This class for MIC values is a quite a special data type: formally it is an ordered factor with valid MIC values as factor levels (to make sure only valid MIC values are retained), but for any mathematical operation it acts as decimal numbers:

This makes it possible to maintain operators that often come with MIC values, such ">=" and "<=", even when filtering using numeric values in data analysis, e.g.:

The following generic functions are implemented for the MIC class: !, !=, %%, %/%, &, *, +, -, /, <, <=, ==, >, >=, ^, |, abs(), acos(), acosh(), all(), any(), asin(), asinh(), atan(), atanh(), ceiling(), cos(), cosh(), cospi(), cummax(), cummin(), cumprod(), cumsum(), digamma(), exp(), expm1(), floor(), gamma(), lgamma(), log(), log1p(), log2(), log10(), max(), mean(), min(), prod(), range(), round(), sign(), signif(), sin(), sinh(), sinpi(), sqrt(), sum(), tan(), tanh(), tanpi(), trigamma() and trunc(). Some functions of the stats package are also implemented: median(), quantile(), mad(), IQR(), fivenum(). Also, boxplot.stats() is supported. Since sd() and var() are non-generic functions, these could not be extended. Use mad() as an alternative, or use e.g. sd(as.numeric(x)) where x is your vector of MIC values.

Using as.double() or as.numeric() on MIC values will remove the operators and return a numeric vector. Do not use as.integer() on MIC values as by the R convention on factors, it will return the index of the factor levels (which is often useless for regular users).

Use droplevels() to drop unused levels. At default, it will return a plain factor. Use droplevels(..., as.mic = TRUE) to maintain the <mic> class.

NA_mic_ is a missing value of the new <mic> class, analogous to e.g. base R's NA_character_.

Stable Lifecycle

The lifecycle of this function is stable. In a stable function, major changes are unlikely. This means that the unlying code will generally evolve by adding new arguments; removing arguments or changing the meaning of existing arguments will be avoided.

If the unlying code needs breaking changes, they will occur gradually. For example, an argument will be deprecated and first continue to work, but will emit a message informing you of the change. Next, typically after at least one newly released version on CRAN, the message will be transformed to an error.

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On our website you can find a comprehensive tutorial about how to conduct AMR data analysis, the complete documentation of all functions and an example analysis using WHONET data.

See also


mic_data <- as.mic(c(">=32", "1.0", "1", "1.00", 8, "<=0.128", "8", "16", "16"))

# this can also coerce combined MIC/RSI values:
as.mic("<=0.002; S") # will return <=0.002

# mathematical processing treats MICs as [numeric] values
all(mic_data < 512)

# interpret MIC values
as.rsi(x = as.mic(2),
       mo ="S. pneumoniae"),
       ab = "AMX",
       guideline = "EUCAST")
as.rsi(x = as.mic(4),
       mo ="S. pneumoniae"),
       ab = "AMX",
       guideline = "EUCAST")

# plot MIC values, see ?plot
plot(mic_data, mo = "E. coli", ab = "cipro")