sir <- random_sir(10)
sir
#> Class 'sir'
-#> [1] S R I I S R S S S R
+#> [1] S S I I I R R S I S
mean_amr_distance(sir)
-#> [1] -0.621059 1.449138 -0.621059 -0.621059 -0.621059 1.449138 -0.621059
-#> [8] -0.621059 -0.621059 1.449138
+#> [1] -0.4743416 -0.4743416 -0.4743416 -0.4743416 -0.4743416 1.8973666
+#> [7] 1.8973666 -0.4743416 -0.4743416 -0.4743416
mic <- random_mic(10)
mic
#> Class 'mic'
-#> [1] 0.005 0.25 1 2 >=16 8 1 1 0.025 4
+#> [1] >=128 0.125 0.01 64 64 8 0.002 1 >=128
+#> [10] <=0.001
mean_amr_distance(mic)
-#> [1] -1.9714967 -0.4147020 0.1369756 0.4128144 1.2403309 0.9644921
-#> [7] 0.1369756 0.1369756 -1.3310188 0.6886533
+#> [1] 1.02853908 -0.44976893 -0.98844311 0.88070828 0.88070828 0.43721587
+#> [7] -1.33169561 -0.00627653 1.02853908 -1.47952641
# equal to the Z-score of their log2:
(log2(mic) - mean(log2(mic))) / sd(log2(mic))
-#> [1] -1.9714967 -0.4147020 0.1369756 0.4128144 1.2403309 0.9644921
-#> [7] 0.1369756 0.1369756 -1.3310188 0.6886533
+#> [1] 1.02853908 -0.44976893 -0.98844311 0.88070828 0.88070828 0.43721587
+#> [7] -1.33169561 -0.00627653 1.02853908 -1.47952641
disk <- random_disk(10)
disk
#> Class 'disk'
-#> [1] 15 15 50 41 10 21 44 42 10 41
+#> [1] 39 35 46 27 13 50 30 49 41 20
mean_amr_distance(disk)
-#> [1] -0.8693350 -0.8693350 1.3196381 0.7567593 -1.1820455 -0.4940825
-#> [7] 0.9443855 0.8193014 -1.1820455 0.7567593
+#> [1] 0.3216338 0.0000000 0.8844928 -0.6432675 -1.7689857 1.2061266
+#> [7] -0.4020422 1.1257182 0.4824506 -1.2061266
y <- data.frame(
id = LETTERS[1:10],
@@ -144,22 +145,22 @@
tobr = random_mic(10, ab = "tobr", mo = "Escherichia coli")
)
y
-#> id amox cipr gent tobr
-#> 1 A S 26 >=4 0.25
-#> 2 B S 20 1 >=4
-#> 3 C S 28 2 0.25
-#> 4 D S 26 <=0.25 0.5
-#> 5 E S 23 2 0.25
-#> 6 F I 31 0.5 1
-#> 7 G R 31 2 >=4
-#> 8 H R 29 1 2
-#> 9 I R 27 <=0.25 >=4
-#> 10 J I 22 0.5 0.25
+#> id amox cipr gent tobr
+#> 1 A R 23 >=4 1
+#> 2 B I 26 <=1 0.5
+#> 3 C S 20 >=4 >=16
+#> 4 D R 27 >=4 0.5
+#> 5 E S 31 >=4 2
+#> 6 F I 27 >=4 >=16
+#> 7 G I 31 <=1 8
+#> 8 H R 30 >=4 4
+#> 9 I S 27 >=4 4
+#> 10 J I 20 >=4 0.5
mean_amr_distance(y)
#> ℹ Calculating mean AMR distance based on columns "amox", "cipr", "gent",
#> and "tobr"
-#> [1] -0.04047504 -0.25789134 -0.08826444 -0.63237868 -0.42488867 0.02453739
-#> [7] 1.18267023 0.72772870 0.36605356 -0.85709171
+#> [1] 0.12237407 -0.92897174 -0.07982527 0.24282355 0.22020411 0.34940813
+#> [7] -0.12306945 0.80126097 0.09975463 -0.70395902
y$amr_distance <- mean_amr_distance(y, where(is.mic))
#> Error in .subset(x, j): invalid subscript type 'list'
y[order(y$amr_distance), ]
@@ -175,17 +176,17 @@
}
#> 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 28 2 0.25 -0.08826444 0.0000000
-#> 2 A S 26 >=4 0.25 -0.04047504 0.0477894
-#> 3 F I 31 0.5 1 0.02453739 0.1128018
-#> 4 B S 20 1 >=4 -0.25789134 0.1696269
-#> 5 E S 23 2 0.25 -0.42488867 0.3366242
-#> 6 I R 27 <=0.25 >=4 0.36605356 0.4543180
-#> 7 D S 26 <=0.25 0.5 -0.63237868 0.5441142
-#> 8 J I 22 0.5 0.25 -0.85709171 0.7688273
-#> 9 H R 29 1 2 0.72772870 0.8159931
-#> 10 G R 31 2 >=4 1.18267023 1.2709347
+#> id amox cipr gent tobr amr_distance check_id_C
+#> 1 C S 20 >=4 >=16 -0.07982527 0.00000000
+#> 2 G I 31 <=1 8 -0.12306945 0.04324419
+#> 3 I S 27 >=4 4 0.09975463 0.17957990
+#> 4 A R 23 >=4 1 0.12237407 0.20219934
+#> 5 E S 31 >=4 2 0.22020411 0.30002938
+#> 6 D R 27 >=4 0.5 0.24282355 0.32264882
+#> 7 F I 27 >=4 >=16 0.34940813 0.42923340
+#> 8 J I 20 >=4 0.5 -0.70395902 0.62413375
+#> 9 B I 26 <=1 0.5 -0.92897174 0.84914647
+#> 10 H R 30 >=4 4 0.80126097 0.88108624
if (require("dplyr")) {
# support for groups
example_isolates %>%
diff --git a/reference/microorganisms.codes.html b/reference/microorganisms.codes.html
index 9107957b6..c0d573007 100644
--- a/reference/microorganisms.codes.html
+++ b/reference/microorganisms.codes.html
@@ -7,7 +7,7 @@
AMR (for R)
- 2.1.1.9216
+ 2.1.1.9217