sir <- random_sir(10)
sir
#> Class 'sir'
-#> [1] R S I I S I I R I I
+#> [1] I I S I I S S S R R
mean_amr_distance(sir)
-#> [1] 1.8973666 -0.4743416 -0.4743416 -0.4743416 -0.4743416 -0.4743416
-#> [7] -0.4743416 1.8973666 -0.4743416 -0.4743416
+#> [1] -0.4743416 -0.4743416 -0.4743416 -0.4743416 -0.4743416 -0.4743416
+#> [7] -0.4743416 -0.4743416 1.8973666 1.8973666
mic <- random_mic(10)
mic
#> Class 'mic'
-#> [1] 0.002 0.5 0.125 128 0.25 0.5 256 0.125 0.125 64
+#> [1] 0.001 256 0.25 0.5 32 0.002 8 0.125 0.002 0.125
mean_amr_distance(mic)
-#> [1] -1.6429088 -0.1673475 -0.5378223 1.3145519 -0.3525849 -0.1673475
-#> [7] 1.4997893 -0.5378223 -0.5378223 1.1293145
+#> [1] -1.2863106 1.6478483 0.0146555 0.1779748 1.1578905 -1.1229914
+#> [7] 0.8312519 -0.1486638 -1.1229914 -0.1486638
# equal to the Z-score of their log2:
(log2(mic) - mean(log2(mic))) / sd(log2(mic))
-#> [1] -1.6429088 -0.1673475 -0.5378223 1.3145519 -0.3525849 -0.1673475
-#> [7] 1.4997893 -0.5378223 -0.5378223 1.1293145
+#> [1] -1.2863106 1.6478483 0.0146555 0.1779748 1.1578905 -1.1229914
+#> [7] 0.8312519 -0.1486638 -1.1229914 -0.1486638
disk <- random_disk(10)
disk
#> Class 'disk'
-#> [1] 40 45 43 14 42 40 37 15 42 11
+#> [1] 41 29 40 12 16 7 45 46 22 9
mean_amr_distance(disk)
-#> [1] 0.5182957 0.8832927 0.7372939 -1.3796886 0.6642945 0.5182957
-#> [7] 0.2992975 -1.3066892 0.6642945 -1.5986868
+#> [1] 0.9256143 0.1488750 0.8608860 -0.9515056 -0.6925925 -1.2751469
+#> [7] 1.1845274 1.2492556 -0.3042229 -1.1456904
y <- data.frame(
id = LETTERS[1:10],
@@ -141,22 +141,22 @@
tobr = random_mic(10, ab = "tobr", mo = "Escherichia coli")
)
y
-#> id amox cipr gent tobr
-#> 1 A R 17 <=0.5 >=8
-#> 2 B I 26 <=0.5 4
-#> 3 C R 18 4 >=8
-#> 4 D R 22 <=0.5 <=0.5
-#> 5 E I 30 <=0.5 1
-#> 6 F S 18 4 2
-#> 7 G I 26 8 2
-#> 8 H S 29 8 1
-#> 9 I I 22 16 1
-#> 10 J S 31 1 4
+#> id amox cipr gent tobr
+#> 1 A R 29 <=1 4
+#> 2 B I 30 4 <=2
+#> 3 C I 26 16 16
+#> 4 D I 18 2 8
+#> 5 E S 29 4 4
+#> 6 F R 31 16 <=2
+#> 7 G S 31 <=1 16
+#> 8 H R 21 16 16
+#> 9 I I 31 16 16
+#> 10 J S 21 <=1 4
mean_amr_distance(y)
#> ℹ Calculating mean AMR distance based on columns "amox", "cipr", "gent",
#> and "tobr"
-#> [1] 0.12957466 -0.14083442 0.55230568 -0.36152662 -0.31478853 -0.33012168
-#> [7] 0.17672648 0.13748045 -0.07163669 0.22282068
+#> [1] 0.04629994 -0.34117383 0.33391321 -0.69063550 -0.19222473 0.50618460
+#> [7] 0.02978803 0.59753857 0.58783703 -0.87752733
y$amr_distance <- mean_amr_distance(y, where(is.mic))
#> Error in .subset(x, j): invalid subscript type 'list'
y[order(y$amr_distance), ]
@@ -172,17 +172,17 @@
}
#> 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 18 4 >=8 0.55230568 0.0000000
-#> 2 J S 31 1 4 0.22282068 0.3294850
-#> 3 G I 26 8 2 0.17672648 0.3755792
-#> 4 H S 29 8 1 0.13748045 0.4148252
-#> 5 A R 17 <=0.5 >=8 0.12957466 0.4227310
-#> 6 I I 22 16 1 -0.07163669 0.6239424
-#> 7 B I 26 <=0.5 4 -0.14083442 0.6931401
-#> 8 E I 30 <=0.5 1 -0.31478853 0.8670942
-#> 9 F S 18 4 2 -0.33012168 0.8824274
-#> 10 D R 22 <=0.5 <=0.5 -0.36152662 0.9138323
+#> id amox cipr gent tobr amr_distance check_id_C
+#> 1 C I 26 16 16 0.33391321 0.0000000
+#> 2 F R 31 16 <=2 0.50618460 0.1722714
+#> 3 I I 31 16 16 0.58783703 0.2539238
+#> 4 H R 21 16 16 0.59753857 0.2636254
+#> 5 A R 29 <=1 4 0.04629994 0.2876133
+#> 6 G S 31 <=1 16 0.02978803 0.3041252
+#> 7 E S 29 4 4 -0.19222473 0.5261379
+#> 8 B I 30 4 <=2 -0.34117383 0.6750870
+#> 9 D I 18 2 8 -0.69063550 1.0245487
+#> 10 J S 21 <=1 4 -0.87752733 1.2114405
if (require("dplyr")) {
# support for groups
example_isolates %>%
diff --git a/reference/microorganisms.codes.html b/reference/microorganisms.codes.html
index 0256a40d..c5216648 100644
--- a/reference/microorganisms.codes.html
+++ b/reference/microorganisms.codes.html
@@ -7,7 +7,7 @@
AMR (for R)
- 2.1.1.9112
+ 2.1.1.9116