sir <- random_sir(10)
sir
#> Class 'sir'
-#> [1] R S R S I I R I I S
+#> [1] R S I I S R I S I I
mean_amr_distance(sir)
-#> [1] 1.449138 -0.621059 1.449138 -0.621059 -0.621059 -0.621059 1.449138
-#> [8] -0.621059 -0.621059 -0.621059
+#> [1] 1.8973666 -0.4743416 -0.4743416 -0.4743416 -0.4743416 1.8973666
+#> [7] -0.4743416 -0.4743416 -0.4743416 -0.4743416
mic <- random_mic(10)
mic
#> Class 'mic'
-#> [1] 0.125 0.0625 1 8 4 0.25 >=32 0.5 0.005 0.25
+#> [1] 16 8 32 0.0625 0.5 >=256 0.002 0.001 1 0.005
mean_amr_distance(mic)
-#> [1] -0.554873825 -0.827456801 0.262875102 1.080624029 0.808041053
-#> [6] -0.282290850 1.625789980 -0.009707874 -1.820709964 -0.282290850
+#> [1] 0.82035688 0.66129723 0.97941652 -0.45212029 0.02505865 1.45659546
+#> [7] -1.24197617 -1.40103582 0.18411830 -1.03171076
# equal to the Z-score of their log2:
(log2(mic) - mean(log2(mic))) / sd(log2(mic))
-#> [1] -0.554873825 -0.827456801 0.262875102 1.080624029 0.808041053
-#> [6] -0.282290850 1.625789980 -0.009707874 -1.820709964 -0.282290850
+#> [1] 0.82035688 0.66129723 0.97941652 -0.45212029 0.02505865 1.45659546
+#> [7] -1.24197617 -1.40103582 0.18411830 -1.03171076
disk <- random_disk(10)
disk
#> Class 'disk'
-#> [1] 26 32 50 20 28 49 12 20 9 42
+#> [1] 7 15 31 28 22 45 13 21 18 41
mean_amr_distance(disk)
-#> [1] -0.19375217 0.22143105 1.46698069 -0.60893538 -0.05535776 1.39778349
-#> [7] -1.16251300 -0.60893538 -1.37010461 0.91340307
+#> [1] -1.4040837 -0.7472024 0.5665601 0.3202296 -0.1724313 1.7161023
+#> [7] -0.9114228 -0.2545415 -0.5008720 1.3876617
y <- data.frame(
id = LETTERS[1:10],
@@ -251,22 +251,22 @@
tobr = random_mic(10, ab = "tobr", mo = "Escherichia coli")
)
y
-#> id amox cipr gent tobr
-#> 1 A S 23 >=4 <=2
-#> 2 B S 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
+#> id amox cipr gent tobr
+#> 1 A R 28 >=16 0.5
+#> 2 B I 31 1 >=8
+#> 3 C R 23 8 0.5
+#> 4 D R 25 >=16 1
+#> 5 E I 17 >=16 2
+#> 6 F I 22 8 4
+#> 7 G S 17 8 <=0.25
+#> 8 H S 19 1 4
+#> 9 I S 19 8 <=0.25
+#> 10 J R 19 <=0.5 4
mean_amr_distance(y)
#> ℹ Calculating mean AMR distance based on columns "amox", "cipr", "gent",
#> and "tobr"
-#> [1] 0.05387469 0.12082470 0.28731436 -0.12784750 -0.20825803 0.12342200
-#> [7] -0.98202095 0.60531151 0.06239096 0.06498826
+#> [1] 0.64013819 0.32815059 0.24520983 0.61863348 -0.14954667 0.11665680
+#> [7] -0.69000290 -0.43800225 -0.58498190 -0.08625517
y$amr_distance <- mean_amr_distance(y, where(is.mic))
#> Error in .subset(x, j): invalid subscript type 'list'
y[order(y$amr_distance), ]
@@ -282,17 +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 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
+#> id amox cipr gent tobr amr_distance check_id_C
+#> 1 C R 23 8 0.5 0.24520983 0.00000000
+#> 2 B I 31 1 >=8 0.32815059 0.08294076
+#> 3 F I 22 8 4 0.11665680 0.12855303
+#> 4 J R 19 <=0.5 4 -0.08625517 0.33146500
+#> 5 D R 25 >=16 1 0.61863348 0.37342365
+#> 6 E I 17 >=16 2 -0.14954667 0.39475650
+#> 7 A R 28 >=16 0.5 0.64013819 0.39492836
+#> 8 H S 19 1 4 -0.43800225 0.68321208
+#> 9 I S 19 8 <=0.25 -0.58498190 0.83019173
+#> 10 G S 17 8 <=0.25 -0.69000290 0.93521273
if (require("dplyr")) {
# support for groups
example_isolates %>%
diff --git a/reference/microorganisms.codes.html b/reference/microorganisms.codes.html
index bc131c9c..8894d33b 100644
--- a/reference/microorganisms.codes.html
+++ b/reference/microorganisms.codes.html
@@ -10,7 +10,7 @@
AMR (for R)
- 2.1.1.9002
+ 2.1.1.9003