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# Calculate the Mean AMR Distance
Calculates a normalised mean for antimicrobial resistance between
multiple observations, to help to identify similar isolates without
comparing antibiograms by hand.
## Usage
``` r
mean_amr_distance(x, ...)
# S3 method for class 'sir'
mean_amr_distance(x, ..., combine_SI = TRUE)
# S3 method for class 'data.frame'
mean_amr_distance(x, ..., combine_SI = TRUE)
amr_distance_from_row(amr_distance, row)
```
## Arguments
- x:
A vector of class [sir](https://amr-for-r.org/reference/as.sir.md),
[mic](https://amr-for-r.org/reference/as.mic.md) or
[disk](https://amr-for-r.org/reference/as.disk.md), or a
[data.frame](https://rdrr.io/r/base/data.frame.html) containing
columns of any of these classes.
- ...:
Variables to select. Supports [tidyselect
language](https://tidyselect.r-lib.org/reference/starts_with.html)
such as `where(is.mic)`, `starts_with(...)`, or `column1:column4`, and
can thus also be [antimicrobial
selectors](https://amr-for-r.org/reference/antimicrobial_selectors.md).
- combine_SI:
A [logical](https://rdrr.io/r/base/logical.html) to indicate whether
all values of S, SDD, and I must be merged into one, so the input only
consists of S+I vs. R (susceptible vs. resistant) - the default is
`TRUE`.
- amr_distance:
The outcome of `mean_amr_distance()`.
- row:
An index, such as a row number.
## Details
The mean AMR distance is effectively [the
Z-score](https://en.wikipedia.org/wiki/Standard_score); a normalised
numeric value to compare AMR test results which can help to identify
similar isolates, without comparing antibiograms by hand.
MIC values (see [`as.mic()`](https://amr-for-r.org/reference/as.mic.md))
are transformed with [`log2()`](https://rdrr.io/r/base/Log.html) first;
their distance is thus calculated as
`(log2(x) - mean(log2(x))) / sd(log2(x))`.
SIR values (see [`as.sir()`](https://amr-for-r.org/reference/as.sir.md))
are transformed using `"S"` = 1, `"I"` = 2, and `"R"` = 3. If
`combine_SI` is `TRUE` (default), the `"I"` will be considered to be 1.
For data sets, the mean AMR distance will be calculated per column,
after which the mean per row will be returned, see *Examples*.
Use `amr_distance_from_row()` to subtract distances from the distance of
one row, see *Examples*.
## Interpretation
Isolates with distances less than 0.01 difference from each other should
be considered similar. Differences lower than 0.025 should be considered
suspicious.
## Examples
``` r
sir <- random_sir(10)
sir
#> Class 'sir'
#> [1] I I R I R S S S I S
mean_amr_distance(sir)
#> [1] -0.4743416 -0.4743416 1.8973666 -0.4743416 1.8973666 -0.4743416
#> [7] -0.4743416 -0.4743416 -0.4743416 -0.4743416
mic <- random_mic(10)
mic
#> Class 'mic'
#> [1] 0.004 2 0.002 0.0001 0.004 0.002 >=4 0.0002 0.032 0.004
mean_amr_distance(mic)
#> [1] -0.2047915 1.5799751 -0.4038557 -1.2641969 -0.2047915 -0.4038557
#> [7] 1.7790393 -1.0651327 0.3924011 -0.2047915
# equal to the Z-score of their log2:
(log2(mic) - mean(log2(mic))) / sd(log2(mic))
#> [1] -0.2047915 1.5799751 -0.4038557 -1.2641969 -0.2047915 -0.4038557
#> [7] 1.7790393 -1.0651327 0.3924011 -0.2047915
disk <- random_disk(10)
disk
#> Class 'disk'
#> [1] 43 12 28 32 22 31 35 25 43 35
mean_amr_distance(disk)
#> [1] 1.30998909 -1.96498364 -0.27467513 0.14790199 -0.90854082 0.04225771
#> [7] 0.46483484 -0.59160798 1.30998909 0.46483484
y <- data.frame(
id = LETTERS[1:10],
amox = random_sir(10, ab = "amox", mo = "Escherichia coli"),
cipr = random_disk(10, ab = "cipr", mo = "Escherichia coli"),
gent = random_mic(10, ab = "gent", mo = "Escherichia coli"),
tobr = random_mic(10, ab = "tobr", mo = "Escherichia coli")
)
y
#> id amox cipr gent tobr
#> 1 A S 31 2 >=16
#> 2 B S 27 <=1 8
#> 3 C R 25 2 4
#> 4 D R 25 <=1 2
#> 5 E I 31 <=1 2
#> 6 F S 32 <=1 8
#> 7 G I 29 2 2
#> 8 H S 18 <=1 4
#> 9 I S 28 <=1 4
#> 10 J R 17 <=1 2
mean_amr_distance(y)
#> ℹ Calculating mean AMR distance based on columns "amox", "cipr", "gent",
#> and "tobr"
#> [1] 0.90606144 -0.03989270 0.66241774 -0.09230226 -0.32300020 0.19914999
#> [7] 0.09893189 -0.70734036 -0.22925499 -0.47477055
y$amr_distance <- mean_amr_distance(y, is.mic(y))
#> ℹ Calculating mean AMR distance based on columns "gent" and "tobr"
y[order(y$amr_distance), ]
#> id amox cipr gent tobr amr_distance
#> 4 D R 25 <=1 2 -0.7848712
#> 5 E I 31 <=1 2 -0.7848712
#> 10 J R 17 <=1 2 -0.7848712
#> 8 H S 18 <=1 4 -0.3105295
#> 9 I S 28 <=1 4 -0.3105295
#> 2 B S 27 <=1 8 0.1638121
#> 6 F S 32 <=1 8 0.1638121
#> 7 G I 29 2 2 0.2502272
#> 3 C R 25 2 4 0.7245688
#> 1 A S 31 2 >=16 1.6732521
if (require("dplyr")) {
y %>%
mutate(
amr_distance = mean_amr_distance(y),
check_id_C = amr_distance_from_row(amr_distance, id == "C")
) %>%
arrange(check_id_C)
}
#> ℹ Calculating mean AMR distance based on columns "amox", "cipr", "gent",
#> and "tobr"
#> id amox cipr gent tobr amr_distance check_id_C
#> 1 C R 25 2 4 0.66241774 0.0000000
#> 2 A S 31 2 >=16 0.90606144 0.2436437
#> 3 F S 32 <=1 8 0.19914999 0.4632678
#> 4 G I 29 2 2 0.09893189 0.5634858
#> 5 B S 27 <=1 8 -0.03989270 0.7023104
#> 6 D R 25 <=1 2 -0.09230226 0.7547200
#> 7 I S 28 <=1 4 -0.22925499 0.8916727
#> 8 E I 31 <=1 2 -0.32300020 0.9854179
#> 9 J R 17 <=1 2 -0.47477055 1.1371883
#> 10 H S 18 <=1 4 -0.70734036 1.3697581
if (require("dplyr")) {
# support for groups
example_isolates %>%
filter(mo_genus() == "Enterococcus" & mo_species() != "") %>%
select(mo, TCY, carbapenems()) %>%
group_by(mo) %>%
mutate(dist = mean_amr_distance(.)) %>%
arrange(mo, dist)
}
#> ℹ Using column 'mo' as input for `mo_genus()`
#> ℹ Using column 'mo' as input for `mo_species()`
#> ℹ For `carbapenems()` using columns 'IPM' (imipenem) and 'MEM' (meropenem)
#> ℹ Calculating mean AMR distance based on columns "TCY", "IPM", and "MEM"
#> # A tibble: 63 × 5
#> # Groups: mo [4]
#> mo TCY IPM MEM dist
#> <mo> <sir> <sir> <sir> <dbl>
#> 1 B_ENTRC_AVIM S S NA 0
#> 2 B_ENTRC_AVIM S S NA 0
#> 3 B_ENTRC_CSSL NA S NA NA
#> 4 B_ENTRC_FACM S S NA -2.66
#> 5 B_ENTRC_FACM S R R -0.423
#> 6 B_ENTRC_FACM S R R -0.423
#> 7 B_ENTRC_FACM NA R R 0.224
#> 8 B_ENTRC_FACM NA R R 0.224
#> 9 B_ENTRC_FACM NA R R 0.224
#> 10 B_ENTRC_FACM NA R R 0.224
#> # ℹ 53 more rows
```