This function calculates a normalised mean for antimicrobial resistance between multiple observations.
Usage
mean_amr_distance(x, ...)
# S3 method for default
mean_amr_distance(x, ...)
# S3 method for mic
mean_amr_distance(x, ...)
# S3 method for disk
mean_amr_distance(x, ...)
# S3 method for rsi
mean_amr_distance(x, combine_SI = TRUE, ...)
# S3 method for data.frame
mean_amr_distance(x, ..., combine_SI = TRUE)
distance_from_row(mean_distance, row)Arguments
- x
a vector of class rsi, rsi or rsi, or a data.frame containing columns of any of these classes
- ...
variables to select (supports tidy selection such as
column1:column4andwhere(is.mic)), and can thus also be antibiotic selectors- combine_SI
a logical to indicate whether all values of S and I must be merged into one, so the input only consists of S+I vs. R (susceptible vs. resistant), defaults to
TRUE- mean_distance
the outcome of
mean_amr_distance()- row
an index, such as a row number
Details
The mean AMR distance is a normalised numeric value to compare AMR test results and can help to identify similar isolates, without comparing antibiograms by hand. For common numeric data this distance is equal to Z scores (the number of standard deviations from the mean).
MIC values (see as.mic()) are transformed with log2() first; their distance is calculated as (log2(x) - mean(log2(x))) / sd(log2(x)).
R/SI values (see as.rsi()) 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 variable, after which the mean of all columns will returned per row (using rowMeans()), see Examples.
Use 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
x <- random_mic(10)
x
#> Class 'mic'
#> [1] 0.025 8 0.0625 16 16 1 0.125 >=32 0.25 0.002
mean_amr_distance(x)
#> [1] -0.9782837 0.7941806 -0.6967299 1.0071679 1.0071679 0.1552190
#> [7] -0.4837427 1.2201551 -0.2707555 -1.7543786
y <- data.frame(
id = LETTERS[1:10],
amox = random_mic(10, ab = "amox", mo = "Escherichia coli"),
cipr = random_mic(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 16 <=0.0625 <=0.25 >=16
#> 2 B >=32 0.125 1 4
#> 3 C 4 <=0.0625 <=0.25 1
#> 4 D >=32 >=1 0.5 8
#> 5 E 8 0.125 2 8
#> 6 F 4 0.25 <=0.25 >=16
#> 7 G 16 <=0.0625 <=0.25 1
#> 8 H 4 0.25 >=4 1
#> 9 I 4 >=1 <=0.25 >=16
#> 10 J <=2 >=1 2 8
mean_amr_distance(y)
#> ℹ Calculating mean AMR distance based on columns "amox", "cipr", "gent",
#> "id" and "tobr"
#> Warning: NAs introduced by coercion
#> [1] -0.04787176 0.30260331 -0.98864537 0.73456530 0.25539334 -0.10496162
#> [7] -0.63509198 -0.05402839 0.19150191 0.34653525
y$amr_distance <- mean_amr_distance(y, where(is.mic))
#> ℹ Calculating mean AMR distance based on columns "amox", "cipr", "gent" and
#> "tobr"
y[order(y$amr_distance), ]
#> id amox cipr gent tobr amr_distance
#> 3 C 4 <=0.0625 <=0.25 1 -0.98864537
#> 7 G 16 <=0.0625 <=0.25 1 -0.63509198
#> 6 F 4 0.25 <=0.25 >=16 -0.10496162
#> 8 H 4 0.25 >=4 1 -0.05402839
#> 1 A 16 <=0.0625 <=0.25 >=16 -0.04787176
#> 9 I 4 >=1 <=0.25 >=16 0.19150191
#> 5 E 8 0.125 2 8 0.25539334
#> 2 B >=32 0.125 1 4 0.30260331
#> 10 J <=2 >=1 2 8 0.34653525
#> 4 D >=32 >=1 0.5 8 0.73456530
if (require("dplyr")) {
y %>%
mutate(
amr_distance = mean_amr_distance(., where(is.mic)),
check_id_C = 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 4 <=0.0625 <=0.25 1 -0.98864537 0.0000000
#> 2 G 16 <=0.0625 <=0.25 1 -0.63509198 0.3535534
#> 3 F 4 0.25 <=0.25 >=16 -0.10496162 0.8836838
#> 4 H 4 0.25 >=4 1 -0.05402839 0.9346170
#> 5 A 16 <=0.0625 <=0.25 >=16 -0.04787176 0.9407736
#> 6 I 4 >=1 <=0.25 >=16 0.19150191 1.1801473
#> 7 E 8 0.125 2 8 0.25539334 1.2440387
#> 8 B >=32 0.125 1 4 0.30260331 1.2912487
#> 9 J <=2 >=1 2 8 0.34653525 1.3351806
#> 10 D >=32 >=1 0.5 8 0.73456530 1.7232107
if (require("dplyr")) {
# support for groups
example_isolates %>%
filter(mo_genus() == "Enterococcus" & mo_species() != "") %>%
select(mo, TCY, carbapenems()) %>%
group_by(mo) %>%
mutate(d = mean_amr_distance(., where(is.rsi))) %>%
arrange(mo, d)
}
#> 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 "IPM", "MEM" and "TCY"
#> # A tibble: 63 × 5
#> # Groups: mo [4]
#> mo TCY IPM MEM d
#> <mo> <rsi> <rsi> <rsi> <dbl>
#> 1 B_ENTRC_AVIM S S NA NaN
#> 2 B_ENTRC_AVIM S S NA NaN
#> 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
#> # … with 53 more rows