sir <- random_sir(10)
sir
#> Class 'sir'
-#> [1] R R R I S R R R R S
+#> [1] I I R S R S R R R S
mean_amr_distance(sir)
-#> [1] 0.621059 0.621059 0.621059 -1.449138 -1.449138 0.621059 0.621059
-#> [8] 0.621059 0.621059 -1.449138
+#> [1] -0.9486833 -0.9486833 0.9486833 -0.9486833 0.9486833 -0.9486833
+#> [7] 0.9486833 0.9486833 0.9486833 -0.9486833
mic <- random_mic(10)
mic
#> Class 'mic'
-#> [1] 64 0.125 32 4 0.0625 0.001 4 128 4 64
+#> [1] 4 >=32 0.005 8 <=0.001 0.025 >=32 2 0.0625
+#> [10] 0.025
mean_amr_distance(mic)
-#> [1] 0.8649908 -0.7925812 0.6808161 0.1282921 -0.9767559 -2.0755022
-#> [7] 0.1282921 1.0491654 0.1282921 0.8649908
+#> [1] 0.6643931 1.2141827 -1.1029707 0.8476563 -1.5284946 -0.6774467
+#> [7] 1.2141827 0.4811299 -0.4351860 -0.6774467
# equal to the Z-score of their log2:
(log2(mic) - mean(log2(mic))) / sd(log2(mic))
-#> [1] 0.8649908 -0.7925812 0.6808161 0.1282921 -0.9767559 -2.0755022
-#> [7] 0.1282921 1.0491654 0.1282921 0.8649908
+#> [1] 0.6643931 1.2141827 -1.1029707 0.8476563 -1.5284946 -0.6774467
+#> [7] 1.2141827 0.4811299 -0.4351860 -0.6774467
disk <- random_disk(10)
disk
#> Class 'disk'
-#> [1] 8 50 16 42 22 38 39 29 34 28
+#> [1] 37 30 32 24 18 32 47 10 37 15
mean_amr_distance(disk)
-#> [1] -1.7802697 1.5281961 -1.1500857 0.8980121 -0.6774478 0.5829202
-#> [7] 0.6616932 -0.1260368 0.2678282 -0.2048098
+#> [1] 0.7726039 0.1580326 0.3336244 -0.3687428 -0.8955181 0.3336244
+#> [7] 1.6505628 -1.5978853 0.7726039 -1.1589058
y <- data.frame(
id = LETTERS[1:10],
@@ -234,22 +235,22 @@
tobr = random_mic(10, ab = "tobr", mo = "Escherichia coli")
)
y
-#> id amox cipr gent tobr
-#> 1 A S 23 4 <=2
-#> 2 B S 17 2 <=2
-#> 3 C R 31 <=0.25 16
-#> 4 D R 18 0.5 <=2
-#> 5 E I 19 <=0.25 8
-#> 6 F I 30 0.5 16
-#> 7 G R 20 1 <=2
-#> 8 H I 31 <=0.25 <=2
-#> 9 I I 27 2 8
-#> 10 J S 19 4 8
+#> id amox cipr gent tobr
+#> 1 A I 18 4 1
+#> 2 B S 17 2 1
+#> 3 C I 23 <=0.5 1
+#> 4 D R 28 <=0.5 1
+#> 5 E S 21 <=0.5 4
+#> 6 F S 25 4 1
+#> 7 G I 28 1 <=0.5
+#> 8 H S 23 4 4
+#> 9 I I 29 >=8 4
+#> 10 J S 24 >=8 1
mean_amr_distance(y)
#> ℹ Calculating mean AMR distance based on columns "amox", "cipr", "gent",
#> and "tobr"
-#> [1] -0.06541043 -0.48291290 0.75508761 -0.23028094 -0.47858500 0.34807060
-#> [7] 0.01180992 -0.33212745 0.33539338 0.13895522
+#> [1] -0.38190897 -0.59575522 -0.53742256 0.55691259 -0.19601880 0.04336306
+#> [7] -0.31201874 0.38476682 0.90237881 0.13570302
y$amr_distance <- mean_amr_distance(y, where(is.mic))
#> Error in .subset(x, j): invalid subscript type 'list'
y[order(y$amr_distance), ]
@@ -265,17 +266,17 @@
}
#> 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 R 31 <=0.25 16 0.75508761 0.0000000
-#> 2 F I 30 0.5 16 0.34807060 0.4070170
-#> 3 I I 27 2 8 0.33539338 0.4196942
-#> 4 J S 19 4 8 0.13895522 0.6161324
-#> 5 G R 20 1 <=2 0.01180992 0.7432777
-#> 6 A S 23 4 <=2 -0.06541043 0.8204980
-#> 7 D R 18 0.5 <=2 -0.23028094 0.9853685
-#> 8 H I 31 <=0.25 <=2 -0.33212745 1.0872151
-#> 9 E I 19 <=0.25 8 -0.47858500 1.2336726
-#> 10 B S 17 2 <=2 -0.48291290 1.2380005
+#> id amox cipr gent tobr amr_distance check_id_C
+#> 1 C I 23 <=0.5 1 -0.53742256 0.00000000
+#> 2 B S 17 2 1 -0.59575522 0.05833266
+#> 3 A I 18 4 1 -0.38190897 0.15551359
+#> 4 G I 28 1 <=0.5 -0.31201874 0.22540382
+#> 5 E S 21 <=0.5 4 -0.19601880 0.34140376
+#> 6 F S 25 4 1 0.04336306 0.58078562
+#> 7 J S 24 >=8 1 0.13570302 0.67312558
+#> 8 H S 23 4 4 0.38476682 0.92218938
+#> 9 D R 28 <=0.5 1 0.55691259 1.09433515
+#> 10 I I 29 >=8 4 0.90237881 1.43980137
if (require("dplyr")) {
# support for groups
example_isolates %>%
diff --git a/reference/microorganisms.codes.html b/reference/microorganisms.codes.html
index 3df051fa..bdcf5ae2 100644
--- a/reference/microorganisms.codes.html
+++ b/reference/microorganisms.codes.html
@@ -10,7 +10,7 @@
AMR (for R)
- 2.0.0.9009
+ 2.0.0.9011