sir <- random_sir(10)
sir
#> Class 'sir'
-#> [1] S R R S I I R S S R
+#> [1] R S R S I I R I I S
mean_amr_distance(sir)
-#> [1] -0.7745967 1.1618950 1.1618950 -0.7745967 -0.7745967 -0.7745967
-#> [7] 1.1618950 -0.7745967 -0.7745967 1.1618950
+#> [1] 1.449138 -0.621059 1.449138 -0.621059 -0.621059 -0.621059 1.449138
+#> [8] -0.621059 -0.621059 -0.621059
mic <- random_mic(10)
mic
#> Class 'mic'
-#> [1] 0.002 >=64 0.125 >=64 2 16 0.005 <=0.001 16
-#> [10] 0.25
+#> [1] 0.125 0.0625 1 8 4 0.25 >=32 0.5 0.005 0.25
mean_amr_distance(mic)
-#> [1] -1.2773949 1.1399840 -0.3137594 1.1399840 0.3323488 0.8169299
-#> [7] -1.0638678 -1.4389220 0.8169299 -0.1522324
+#> [1] -0.554873825 -0.827456801 0.262875102 1.080624029 0.808041053
+#> [6] -0.282290850 1.625789980 -0.009707874 -1.820709964 -0.282290850
# equal to the Z-score of their log2:
(log2(mic) - mean(log2(mic))) / sd(log2(mic))
-#> [1] -1.2773949 1.1399840 -0.3137594 1.1399840 0.3323488 0.8169299
-#> [7] -1.0638678 -1.4389220 0.8169299 -0.1522324
+#> [1] -0.554873825 -0.827456801 0.262875102 1.080624029 0.808041053
+#> [6] -0.282290850 1.625789980 -0.009707874 -1.820709964 -0.282290850
disk <- random_disk(10)
disk
#> Class 'disk'
-#> [1] 34 26 14 50 7 45 18 40 19 24
+#> [1] 26 32 50 20 28 49 12 20 9 42
mean_amr_distance(disk)
-#> [1] 0.4467074 -0.1205401 -0.9714114 1.5812024 -1.4677529 1.2266727
-#> [7] -0.6877876 0.8721430 -0.6168817 -0.2623520
+#> [1] -0.19375217 0.22143105 1.46698069 -0.60893538 -0.05535776 1.39778349
+#> [7] -1.16251300 -0.60893538 -1.37010461 0.91340307
y <- data.frame(
id = LETTERS[1:10],
@@ -252,22 +251,22 @@
tobr = random_mic(10, ab = "tobr", mo = "Escherichia coli")
)
y
-#> id amox cipr gent tobr
-#> 1 A R 30 8 2
-#> 2 B I 24 <=0.5 4
-#> 3 C S 19 2 <=0.5
-#> 4 D I 21 <=0.5 1
-#> 5 E I 22 >=16 1
-#> 6 F I 28 8 2
-#> 7 G I 19 4 <=0.5
-#> 8 H I 29 <=0.5 <=0.5
-#> 9 I I 26 >=16 <=0.5
-#> 10 J R 21 8 1
+#> id amox cipr gent tobr
+#> 1 A S 23 >=4 <=2
+#> 2 B S 27 1 4
+#> 3 C I 26 >=4 <=2
+#> 4 D I 19 1 >=16
+#> 5 E S 20 2 4
+#> 6 F I 25 0.5 >=16
+#> 7 G S 18 0.5 <=2
+#> 8 H R 19 2 >=16
+#> 9 I R 22 1 <=2
+#> 10 J R 20 0.5 8
mean_amr_distance(y)
#> ℹ Calculating mean AMR distance based on columns "amox", "cipr", "gent",
#> and "tobr"
-#> [1] 1.23945841 0.03375462 -0.73801392 -0.62254823 0.04563698 0.52522389
-#> [7] -0.61650763 -0.37448920 0.05108107 0.45640401
+#> [1] 0.05387469 0.12082470 0.28731436 -0.12784750 -0.20825803 0.12342200
+#> [7] -0.98202095 0.60531151 0.06239096 0.06498826
y$amr_distance <- mean_amr_distance(y, where(is.mic))
#> Error in .subset(x, j): invalid subscript type 'list'
y[order(y$amr_distance), ]
@@ -283,17 +282,17 @@
}
#> 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 S 19 2 <=0.5 -0.73801392 0.0000000
-#> 2 D I 21 <=0.5 1 -0.62254823 0.1154657
-#> 3 G I 19 4 <=0.5 -0.61650763 0.1215063
-#> 4 H I 29 <=0.5 <=0.5 -0.37448920 0.3635247
-#> 5 B I 24 <=0.5 4 0.03375462 0.7717685
-#> 6 E I 22 >=16 1 0.04563698 0.7836509
-#> 7 I I 26 >=16 <=0.5 0.05108107 0.7890950
-#> 8 J R 21 8 1 0.45640401 1.1944179
-#> 9 F I 28 8 2 0.52522389 1.2632378
-#> 10 A R 30 8 2 1.23945841 1.9774723
+#> id amox cipr gent tobr amr_distance check_id_C
+#> 1 C I 26 >=4 <=2 0.28731436 0.0000000
+#> 2 F I 25 0.5 >=16 0.12342200 0.1638924
+#> 3 B S 27 1 4 0.12082470 0.1664897
+#> 4 J R 20 0.5 8 0.06498826 0.2223261
+#> 5 I R 22 1 <=2 0.06239096 0.2249234
+#> 6 A S 23 >=4 <=2 0.05387469 0.2334397
+#> 7 H R 19 2 >=16 0.60531151 0.3179971
+#> 8 D I 19 1 >=16 -0.12784750 0.4151619
+#> 9 E S 20 2 4 -0.20825803 0.4955724
+#> 10 G S 18 0.5 <=2 -0.98202095 1.2693353
if (require("dplyr")) {
# support for groups
example_isolates %>%
diff --git a/reference/microorganisms.codes.html b/reference/microorganisms.codes.html
index e4e70e08..bc131c9c 100644
--- a/reference/microorganisms.codes.html
+++ b/reference/microorganisms.codes.html
@@ -10,7 +10,7 @@
AMR (for R)
- 2.1.1.9001
+ 2.1.1.9002