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@@ -62,12 +62,10 @@ droplevels(x, as.mic = FALSE, ...)
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- as.mic:
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A [logical](https://rdrr.io/r/base/logical.html) to indicate whether
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the `mic` class should be kept - the default is `TRUE` for
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`rescale_mic()` and `FALSE` for
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[`droplevels()`](https://rdrr.io/pkg/data.table/man/fdroplevels.html).
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When setting this to `FALSE` in `rescale_mic()`, the output will have
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factor levels that acknowledge `mic_range`.
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A \[logical\] to indicate whether the \`mic\` class should be kept -
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the default is \`TRUE\` for \[rescale_mic()\] and \`FALSE\` for
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\[droplevels()\]. When setting this to \`FALSE\` in \[rescale_mic()\],
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the output will have factor levels that acknowledge \`mic_range\`.
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- ...:
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@@ -75,101 +73,71 @@ droplevels(x, as.mic = FALSE, ...)
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## Value
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Ordered [factor](https://rdrr.io/pkg/data.table/man/fctr.html) with
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additional class `mic`, that in mathematical operations acts as a
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[numeric](https://rdrr.io/r/base/numeric.html) vector. Bear in mind that
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Ordered \[factor\] with additional class \[\`mic\`\], that in
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mathematical operations acts as a \[numeric\] vector. Bear in mind that
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the outcome of any mathematical operation on MICs will return a
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[numeric](https://rdrr.io/r/base/numeric.html) value.
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\[numeric\] value.
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## Details
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To interpret MIC values as SIR values, use
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[`as.sir()`](https://amr-for-r.org/reference/as.sir.md) on MIC values.
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It supports guidelines from EUCAST (2011-2026) and CLSI (2011-2026).
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To interpret MIC values as SIR values, use \[as.sir()\] on MIC values.
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It supports guidelines from EUCAST (\`r min(as.integer(gsub("\[^0-9\]",
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"", subset(clinical_breakpoints, guideline
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This class for MIC values is a quite a special data type: formally it is
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an ordered [factor](https://rdrr.io/pkg/data.table/man/fctr.html) with
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valid MIC values as
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[factor](https://rdrr.io/pkg/data.table/man/fctr.html) levels (to make
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sure only valid MIC values are retained), but for any mathematical
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an ordered \[factor\] with valid MIC values as \[factor\] levels (to
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make sure only valid MIC values are retained), but for any mathematical
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operation it acts as decimal numbers:
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x <- random_mic(10)
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x
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#> Class <mic>
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#> [1] 16 1 8 8 64 >=128 0.0625 32 32 16
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“\` x \<- random_mic(10) x \#\> Class \<mic\> \#\> \[1\] 16 1 8 8 64
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\>=128 0.0625 32 32 16
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is.factor(x)
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#> [1] TRUE
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is.factor(x) \#\> \[1\] TRUE
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x[1] * 2
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#> [1] 32
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x\[1\] \* 2 \#\> \[1\] 32
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median(x)
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#> [1] 26
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median(x) \#\> \[1\] 26 “\`
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This makes it possible to maintain operators that often come with MIC
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values, such "\>=" and "\<=", even when filtering using
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[numeric](https://rdrr.io/r/base/numeric.html) values in data analysis,
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e.g.:
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values, such "\>=" and "\<=", even when filtering using \[numeric\]
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values in data analysis, e.g.:
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x[x > 4]
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#> Class <mic>
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#> [1] 16 8 8 64 >=128 32 32 16
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“\` x\[x \> 4\] \#\> Class \<mic\> \#\> \[1\] 16 8 8 64 \>=128 32 32 16
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df <- data.frame(x, hospital = "A")
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subset(df, x > 4) # or with dplyr: df %>% filter(x > 4)
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#> x hospital
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#> 1 16 A
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#> 5 64 A
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#> 6 >=128 A
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#> 8 32 A
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#> 9 32 A
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#> 10 16 A
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df \<- data.frame(x, hospital = "A") subset(df, x \> 4) \# or with
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dplyr: df \#\> x hospital \#\> 1 16 A \#\> 5 64 A \#\> 6 \>=128 A \#\> 8
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32 A \#\> 9 32 A \#\> 10 16 A “\`
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All so-called [group generic
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functions](https://rdrr.io/r/base/groupGeneric.html) are implemented for
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the MIC class (such as `!`, `!=`, `<`, `>=`,
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[`exp()`](https://rdrr.io/r/base/Log.html),
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[`log2()`](https://rdrr.io/r/base/Log.html)). Some mathematical
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functions are also implemented (such as
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[`quantile()`](https://rdrr.io/r/stats/quantile.html),
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[`median()`](https://rdrr.io/r/stats/median.html),
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[`fivenum()`](https://rdrr.io/r/stats/fivenum.html)). Since
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[`sd()`](https://rdrr.io/r/stats/sd.html) and
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[`var()`](https://rdrr.io/r/stats/cor.html) are non-generic functions,
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these could not be extended. Use
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[`mad()`](https://rdrr.io/r/stats/mad.html) as an alternative, or use
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e.g. `sd(as.numeric(x))` where `x` is your vector of MIC values.
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All so-called \[group generic functions\]\[groupGeneric()\] are
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implemented for the MIC class (such as \`!\`, \`!=\`, \`\<\`, \`\>=\`,
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\[exp()\], \[log2()\]). Some mathematical functions are also implemented
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(such as \[quantile()\], \[median()\], \[fivenum()\]). Since \[sd()\]
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and \[var()\] are non-generic functions, these could not be extended.
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Use \[mad()\] as an alternative, or use e.g. \`sd(as.numeric(x))\` where
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\`x\` is your vector of MIC values.
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Using [`as.double()`](https://rdrr.io/r/base/double.html) or
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[`as.numeric()`](https://rdrr.io/r/base/numeric.html) on MIC values will
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remove the operators and return a numeric vector. Do **not** use
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[`as.integer()`](https://rdrr.io/r/base/integer.html) on MIC values as
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by the R convention on
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[factor](https://rdrr.io/pkg/data.table/man/fctr.html)s, it will return
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the index of the factor levels (which is often useless for regular
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users).
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Using \[as.double()\] or \[as.numeric()\] on MIC values will remove the
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operators and return a numeric vector. Do \*\*not\*\* use
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\[as.integer()\] on MIC values as by the R convention on \[factor\]s, it
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will return the index of the factor levels (which is often useless for
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regular users).
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The function `is.mic()` detects if the input contains class `mic`. If
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the input is a [data.frame](https://rdrr.io/r/base/data.frame.html) or
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[list](https://rdrr.io/r/base/list.html), it iterates over all
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columns/items and returns a
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[logical](https://rdrr.io/r/base/logical.html) vector.
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The function \[is.mic()\] detects if the input contains class \`mic\`.
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If the input is a \[data.frame\] or \[list\], it iterates over all
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columns/items and returns a \[logical\] vector.
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Use
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[`droplevels()`](https://rdrr.io/pkg/data.table/man/fdroplevels.html) to
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drop unused levels. At default, it will return a plain factor. Use
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`droplevels(..., as.mic = TRUE)` to maintain the `mic` class.
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Use \[droplevels()\] to drop unused levels. At default, it will return a
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plain factor. Use \`droplevels(..., as.mic = TRUE)\` to maintain the
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\`mic\` class.
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With `rescale_mic()`, existing MIC ranges can be limited to a defined
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With \[rescale_mic()\], existing MIC ranges can be limited to a defined
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range of MIC values. This can be useful to better compare MIC
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distributions.
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For `ggplot2`, use one of the
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[`scale_*_mic()`](https://amr-for-r.org/reference/plot.md) functions to
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plot MIC values. They allows custom MIC ranges and to plot intermediate
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log2 levels for missing MIC values.
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For \`ggplot2\`, use one of the
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\[\`scale\_\*\_mic()\`\]\[scale_x_mic()\] functions to plot MIC values.
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They allows custom MIC ranges and to plot intermediate log2 levels for
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missing MIC values.
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`NA_mic_` is a missing value of the new `mic` class, analogous to e.g.
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base R's [`NA_character_`](https://rdrr.io/r/base/NA.html).
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