mean_amr_distance(x, ...)
-# S3 method for rsi
+# S3 method for sir
mean_amr_distance(x, ..., combine_SI = TRUE)
# S3 method for data.frame
@@ -152,7 +152,7 @@
Arguments
- x
-a vector of class rsi, mic or disk, or a data.frame containing columns of any of these classes
a vector of class sir, mic or disk, or a data.frame containing columns of any of these classes
- ...
@@ -175,7 +175,7 @@
Details
The mean AMR distance is effectively the Z-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()) are transformed with log2() first; their distance is thus 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.
+SIR values (see as.sir()) 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.
@@ -188,72 +188,73 @@
Examples
-
rsi <- random_rsi(10)
-rsi
-#> Class 'rsi'
-#> [1] S S S I I R S S I I
-mean_amr_distance(rsi)
-#> [1] -0.3162278 -0.3162278 -0.3162278 -0.3162278 -0.3162278 2.8460499
-#> [7] -0.3162278 -0.3162278 -0.3162278 -0.3162278
+ sir <- random_sir(10)
+sir
+#> Class 'sir'
+#> [1] R I S S I R S R R I
+mean_amr_distance(sir)
+#> [1] 1.1618950 -0.7745967 -0.7745967 -0.7745967 -0.7745967 1.1618950
+#> [7] -0.7745967 1.1618950 1.1618950 -0.7745967
mic <- random_mic(10)
mic
#> Class 'mic'
-#> [1] 0.25 16 0.01 0.002 2 0.002 0.025 >=256 16 16
+#> [1] 8 4 <=0.0625 256 32 1 <=0.0625 256
+#> [9] 64 0.5
mean_amr_distance(mic)
-#> [1] -0.1484568 0.8300945 -0.9058320 -1.2845196 0.3408189 -1.2845196
-#> [7] -0.6902363 1.4824620 0.8300945 0.8300945
+#> [1] 0.15561961 -0.06669412 -1.40057649 1.26718825 0.60024707 -0.51132157
+#> [7] -1.40057649 1.26718825 0.82256079 -0.73363530
# equal to the Z-score of their log2:
(log2(mic) - mean(log2(mic))) / sd(log2(mic))
-#> [1] -0.1484568 0.8300945 -0.9058320 -1.2845196 0.3408189 -1.2845196
-#> [7] -0.6902363 1.4824620 0.8300945 0.8300945
+#> [1] 0.15561961 -0.06669412 -1.40057649 1.26718825 0.60024707 -0.51132157
+#> [7] -1.40057649 1.26718825 0.82256079 -0.73363530
disk <- random_disk(10)
disk
#> Class 'disk'
-#> [1] 44 8 43 18 44 10 32 16 14 6
+#> [1] 47 26 16 48 35 11 26 27 44 49
mean_amr_distance(disk)
-#> [1] 1.3116316 -0.9917215 1.2476496 -0.3519012 1.3116316 -0.8637574
-#> [7] 0.5438473 -0.4798652 -0.6078293 -1.1196855
+#> [1] 1.0232507 -0.5007397 -1.2264494 1.0958217 0.1523990 -1.5893042
+#> [7] -0.5007397 -0.4281687 0.8055378 1.1683926
y <- data.frame(
id = LETTERS[1:10],
- amox = random_rsi(10, ab = "amox", mo = "Escherichia coli"),
+ 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 R 25 4 2
-#> 2 B S 28 8 2
-#> 3 C I 27 16 >=4
-#> 4 D R 25 1 0.5
-#> 5 E S 29 <=0.25 1
-#> 6 F I 30 1 0.5
-#> 7 G I 31 4 >=4
-#> 8 H I 31 16 >=4
-#> 9 I R 19 1 <=0.25
-#> 10 J I 19 8 2
+#> 1 A R 22 2 <=0.25
+#> 2 B S 26 1 >=8
+#> 3 C S 17 1 2
+#> 4 D I 17 2 0.5
+#> 5 E R 19 0.5 0.5
+#> 6 F S 23 1 1
+#> 7 G S 21 <=0.25 4
+#> 8 H I 26 2 1
+#> 9 I I 19 2 1
+#> 10 J S 18 1 >=8
mean_amr_distance(y)
#> ℹ Calculating mean AMR distance based on columns "amox", "cipr", "gent" and
#> "tobr"
-#> [1] 0.4205821 0.1957997 0.4383306 -0.1767834 -0.5439764 -0.4135734
-#> [7] 0.4143153 0.6629379 -0.6880658 -0.3095667
+#> [1] 0.42479243 0.60367076 -0.35088351 -0.39086870 -0.15337200 -0.05446405
+#> [7] -0.41325021 0.41799269 -0.09780504 0.01418764
y$amr_distance <- mean_amr_distance(y, where(is.mic))
#> ℹ Calculating mean AMR distance based on columns "gent" and "tobr"
y[order(y$amr_distance), ]
#> id amox cipr gent tobr amr_distance
-#> 9 I R 19 1 <=0.25 -1.2696534
-#> 5 E S 29 <=0.25 1 -1.0694127
-#> 4 D R 25 1 0.5 -0.9209105
-#> 6 F I 30 1 0.5 -0.9209105
-#> 1 A R 25 4 2 0.2738205
-#> 2 B S 28 8 2 0.5224431
-#> 10 J I 19 8 2 0.5224431
-#> 7 G I 31 4 >=4 0.6225634
-#> 3 C I 27 16 >=4 1.1198086
-#> 8 H I 31 16 >=4 1.1198086
+#> 5 E R 19 0.5 0.5 -0.99015989
+#> 7 G S 21 <=0.25 4 -0.61880376
+#> 1 A R 22 2 <=0.25 -0.27594337
+#> 6 F S 23 1 1 -0.19597299
+#> 4 D I 17 2 0.5 0.01544239
+#> 3 C S 17 1 2 0.09541277
+#> 8 H I 26 2 1 0.30682815
+#> 9 I I 19 2 1 0.30682815
+#> 2 B S 26 1 >=8 0.67818428
+#> 10 J S 18 1 >=8 0.67818428
if (require("dplyr")) {
y %>%
@@ -266,16 +267,16 @@
#> 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 I 27 16 >=4 0.4383306 0.00000000
-#> 2 A R 25 4 2 0.4205821 0.01774851
-#> 3 G I 31 4 >=4 0.4143153 0.02401530
-#> 4 H I 31 16 >=4 0.6629379 0.22460728
-#> 5 B S 28 8 2 0.1957997 0.24253093
-#> 6 D R 25 1 0.5 -0.1767834 0.61511400
-#> 7 J I 19 8 2 -0.3095667 0.74789731
-#> 8 F I 30 1 0.5 -0.4135734 0.85190407
-#> 9 E S 29 <=0.25 1 -0.5439764 0.98230701
-#> 10 I R 19 1 <=0.25 -0.6880658 1.12639638
+#> 1 C S 17 1 2 -0.35088351 0.00000000
+#> 2 D I 17 2 0.5 -0.39086870 0.03998519
+#> 3 G S 21 <=0.25 4 -0.41325021 0.06236670
+#> 4 E R 19 0.5 0.5 -0.15337200 0.19751151
+#> 5 I I 19 2 1 -0.09780504 0.25307847
+#> 6 F S 23 1 1 -0.05446405 0.29641946
+#> 7 J S 18 1 >=8 0.01418764 0.36507115
+#> 8 H I 26 2 1 0.41799269 0.76887620
+#> 9 A R 22 2 <=0.25 0.42479243 0.77567594
+#> 10 B S 26 1 >=8 0.60367076 0.95455427
if (require("dplyr")) {
# support for groups
example_isolates %>%
@@ -292,7 +293,7 @@
#> # A tibble: 63 × 5
#> # Groups: mo [4]
#> mo TCY IPM MEM dist
-#> <mo> <rsi> <rsi> <rsi> <dbl>
+#> <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
diff --git a/reference/microorganisms.codes.html b/reference/microorganisms.codes.html
index 2d21310a1..df64dd122 100644
--- a/reference/microorganisms.codes.html
+++ b/reference/microorganisms.codes.html
@@ -10,7 +10,7 @@
AMR (for R)
- 1.8.2.9095
+ 1.8.2.9096
Author
diff --git a/reference/mo_property.html b/reference/mo_property.html
index 8ac0975ec..ecc85940c 100644
--- a/reference/mo_property.html
+++ b/reference/mo_property.html
@@ -10,7 +10,7 @@
AMR (for R)
-
1.8.2.9095
+
1.8.2.9096
See also
diff --git a/reference/mo_source.html b/reference/mo_source.html
index 1d3808e25..7605427af 100644
--- a/reference/mo_source.html
+++ b/reference/mo_source.html
@@ -12,7 +12,7 @@ This is the fastest way to have your organisation (or analysis) specific codes p
AMR (for R)
-
1.8.2.9095
+
1.8.2.9096