This transforms a vector to a new class mic
, which is an ordered factor with valid minimum inhibitory concentrations (MIC) as levels. Invalid MIC values will be translated as NA
with a warning.
as.mic(x, na.rm = FALSE) is.mic(x)
x | vector |
---|---|
na.rm | a logical indicating whether missing values should be removed |
Ordered factor with additional class mic
To interpret MIC values as RSI values, use as.rsi()
on MIC values. It supports guidelines from EUCAST and CLSI.
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, a argument will be deprecated and first continue to work, but will emit an 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.
On our website https://msberends.github.io/AMR/ 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. As we would like to better understand the backgrounds and needs of our users, please participate in our survey!
mic_data <- as.mic(c(">=32", "1.0", "1", "1.00", 8, "<=0.128", "8", "16", "16")) is.mic(mic_data) # this can also coerce combined MIC/RSI values: as.mic("<=0.002; S") # will return <=0.002 # interpret MIC values as.rsi(x = as.mic(2), mo = as.mo("S. pneumoniae"), ab = "AMX", guideline = "EUCAST") as.rsi(x = as.mic(4), mo = as.mo("S. pneumoniae"), ab = "AMX", guideline = "EUCAST") plot(mic_data) barplot(mic_data)