rsi <- random_rsi(10)
rsi
#> Class 'rsi'
-#> [1] R S S I S I R I S I
+#> [1] I I I I S S R S S R
mean_amr_distance(rsi)
-#> [1] 1.8973666 -0.4743416 -0.4743416 -0.4743416 -0.4743416 -0.4743416
-#> [7] 1.8973666 -0.4743416 -0.4743416 -0.4743416
+#> [1] -0.4743416 -0.4743416 -0.4743416 -0.4743416 -0.4743416 -0.4743416
+#> [7] 1.8973666 -0.4743416 -0.4743416 1.8973666
mic <- random_mic(10)
mic
#> Class 'mic'
-#> [1] 1 2 <=0.001 <=0.001 <=0.001 >=16 0.002 0.125 0.125
-#> [10] 8
+#> [1] 32 4 0.005 <=0.002 8 0.125 16 64 >=256
+#> [10] 0.025
mean_amr_distance(mic)
-#> [1] 0.6534187 0.8306741 -1.1130696 -1.1130696 -1.1130696 1.3624400
-#> [7] -0.9358143 0.1216528 0.1216528 1.1851847
+#> [1] 0.7759961 0.2799572 -1.3146188 -1.5331947 0.4453035 -0.5467743
+#> [7] 0.6106498 0.9413425 1.2720351 -0.9306965
# equal to the Z-score of their log2:
(log2(mic) - mean(log2(mic))) / sd(log2(mic))
-#> [1] 0.6534187 0.8306741 -1.1130696 -1.1130696 -1.1130696 1.3624400
-#> [7] -0.9358143 0.1216528 0.1216528 1.1851847
+#> [1] 0.7759961 0.2799572 -1.3146188 -1.5331947 0.4453035 -0.5467743
+#> [7] 0.6106498 0.9413425 1.2720351 -0.9306965
disk <- random_disk(10)
disk
#> Class 'disk'
-#> [1] 19 41 24 49 8 47 23 25 8 21
+#> [1] 34 40 49 17 45 36 20 20 48 18
mean_amr_distance(disk)
-#> [1] -0.5123570 0.9905569 -0.1707857 1.5370711 -1.2638140 1.4004425
-#> [7] -0.2390999 -0.1024714 -1.2638140 -0.3757285
+#> [1] 0.1006272 0.5650602 1.2617097 -1.2152664 0.9520877 0.2554382
+#> [7] -0.9830499 -0.9830499 1.1843042 -1.1378609
y <- data.frame(
id = LETTERS[1:10],
@@ -226,35 +226,35 @@
)
y
#> id amox cipr gent tobr
-#> 1 A R 20 1 <=0.5
-#> 2 B I 22 >=2 >=16
-#> 3 C S 24 >=2 1
-#> 4 D R 26 >=2 1
-#> 5 E S 21 0.5 4
-#> 6 F R 20 0.5 <=0.5
-#> 7 G I 23 >=2 2
-#> 8 H I 24 1 2
-#> 9 I I 19 <=0.25 <=0.5
-#> 10 J S 27 <=0.25 <=0.5
+#> 1 A I 19 <=0.25 <=0.5
+#> 2 B R 21 0.5 2
+#> 3 C I 30 1 2
+#> 4 D S 30 8 >=4
+#> 5 E R 18 <=0.25 2
+#> 6 F I 29 16 >=4
+#> 7 G S 24 4 <=0.5
+#> 8 H I 21 4 1
+#> 9 I I 28 2 >=4
+#> 10 J S 26 0.5 <=0.5
mean_amr_distance(y)
#> ℹ Calculating mean AMR distance based on columns "amox", "cipr", "gent" and
#> "tobr"
-#> [1] -0.05259924 0.57930600 0.15889055 0.86335651 -0.22456313 -0.25597154
-#> [7] 0.21726521 0.10735130 -1.07035142 -0.32268424
+#> [1] -1.0443988 0.1699505 0.1816530 0.7332860 -0.1102573 0.7972126
+#> [7] -0.3020597 -0.2666282 0.3891516 -0.5479098
y$amr_distance <- mean_amr_distance(y, where(is.mic))
#> ℹ Calculating mean AMR distance based on columns "gent" and "tobr"
y[order(y$amr_distance), ]
#> id amox cipr gent tobr amr_distance
-#> 9 I I 19 <=0.25 <=0.5 -1.1572729
-#> 10 J S 27 <=0.25 <=0.5 -1.1572729
-#> 6 F R 20 0.5 <=0.5 -0.7505283
-#> 1 A R 20 1 <=0.5 -0.3437837
-#> 5 E S 21 0.5 4 0.1604701
-#> 8 H I 24 1 2 0.2635486
-#> 3 C S 24 >=2 1 0.3666271
-#> 4 D R 26 >=2 1 0.3666271
-#> 7 G I 23 >=2 2 0.6702932
-#> 2 B I 22 >=2 >=16 1.5812916
+#> 1 A I 19 <=0.25 <=0.5 -1.24603939
+#> 10 J S 26 0.5 <=0.5 -1.01004560
+#> 5 E R 18 <=0.25 2 -0.45546997
+#> 7 G S 24 4 <=0.5 -0.30206422
+#> 2 B R 21 0.5 2 -0.21947618
+#> 3 C I 30 1 2 0.01651761
+#> 8 H I 21 4 1 0.09322048
+#> 9 I I 28 2 >=4 0.64779611
+#> 4 D S 30 8 >=4 1.11978369
+#> 6 F I 29 16 >=4 1.35577748
if (require("dplyr")) {
y %>%
@@ -267,16 +267,16 @@
#> 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 24 >=2 1 0.15889055 0.00000000
-#> 2 H I 24 1 2 0.10735130 0.05153924
-#> 3 G I 23 >=2 2 0.21726521 0.05837466
-#> 4 A R 20 1 <=0.5 -0.05259924 0.21148979
-#> 5 E S 21 0.5 4 -0.22456313 0.38345368
-#> 6 F R 20 0.5 <=0.5 -0.25597154 0.41486209
-#> 7 B I 22 >=2 >=16 0.57930600 0.42041545
-#> 8 J S 27 <=0.25 <=0.5 -0.32268424 0.48157478
-#> 9 D R 26 >=2 1 0.86335651 0.70446596
-#> 10 I I 19 <=0.25 <=0.5 -1.07035142 1.22924196
+#> 1 C I 30 1 2 0.1816530 0.00000000
+#> 2 B R 21 0.5 2 0.1699505 0.01170248
+#> 3 I I 28 2 >=4 0.3891516 0.20749866
+#> 4 E R 18 <=0.25 2 -0.1102573 0.29191026
+#> 5 H I 21 4 1 -0.2666282 0.44828121
+#> 6 G S 24 4 <=0.5 -0.3020597 0.48371268
+#> 7 D S 30 8 >=4 0.7332860 0.55163304
+#> 8 F I 29 16 >=4 0.7972126 0.61555964
+#> 9 J S 26 0.5 <=0.5 -0.5479098 0.72956278
+#> 10 A I 19 <=0.25 <=0.5 -1.0443988 1.22605173
if (require("dplyr")) {
# support for groups
example_isolates %>%
diff --git a/reference/microorganisms.codes.html b/reference/microorganisms.codes.html
index 871cef29..3a9538b4 100644
--- a/reference/microorganisms.codes.html
+++ b/reference/microorganisms.codes.html
@@ -10,7 +10,7 @@
AMR (for R)
- 1.8.2.9084
+ 1.8.2.9085