sir <- random_sir(10)
sir
#> Class 'sir'
-#> [1] R R R S R I S I I S
+#> [1] I R S S S R R I R I
mean_amr_distance(sir)
-#> [1] 1.1618950 1.1618950 1.1618950 -0.7745967 1.1618950 -0.7745967
-#> [7] -0.7745967 -0.7745967 -0.7745967 -0.7745967
+#> [1] -0.7745967 1.1618950 -0.7745967 -0.7745967 -0.7745967 1.1618950
+#> [7] 1.1618950 -0.7745967 1.1618950 -0.7745967
mic <- random_mic(10)
mic
#> Class 'mic'
-#> [1] 0.125 4 16 >=256 0.002 0.01 0.0625 2 >=256 128
+#> [1] <=0.001 0.025 32 0.01 0.0625 0.125 0.5 0.01 8
+#> [10] 2
mean_amr_distance(mic)
-#> [1] -0.60649465 0.20453613 0.52894844 1.17777307 -1.57418159 -1.19755056
-#> [7] -0.76870081 0.04232998 1.17777307 1.01556691
+#> [1] -1.5257422 -0.5427941 1.6420106 -0.8226018 -0.2629864 -0.0513201
+#> [7] 0.3720126 -0.8226018 1.2186779 0.7953453
# equal to the Z-score of their log2:
(log2(mic) - mean(log2(mic))) / sd(log2(mic))
-#> [1] -0.60649465 0.20453613 0.52894844 1.17777307 -1.57418159 -1.19755056
-#> [7] -0.76870081 0.04232998 1.17777307 1.01556691
+#> [1] -1.5257422 -0.5427941 1.6420106 -0.8226018 -0.2629864 -0.0513201
+#> [7] 0.3720126 -0.8226018 1.2186779 0.7953453
disk <- random_disk(10)
disk
#> Class 'disk'
-#> [1] 39 30 35 42 40 19 36 39 47 33
+#> [1] 16 44 41 26 44 46 48 50 48 40
mean_amr_distance(disk)
-#> [1] 0.3924186 -0.7848373 -0.1308062 0.7848373 0.5232249 -2.2237057
-#> [7] 0.0000000 0.3924186 1.4388684 -0.3924186
+#> [1] -2.23059935 0.33963858 0.06425595 -1.31265723 0.33963858 0.52322701
+#> [7] 0.70681543 0.89040385 0.70681543 -0.02753826
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 I 30 1 <=0.25
-#> 2 B S 23 1 16
-#> 3 C I 17 8 1
-#> 4 D R 20 >=16 16
-#> 5 E S 25 <=0.5 0.5
-#> 6 F S 19 1 4
-#> 7 G I 27 <=0.5 <=0.25
-#> 8 H R 26 8 16
-#> 9 I R 18 2 4
-#> 10 J R 20 8 0.5
+#> id amox cipr gent tobr
+#> 1 A I 21 >=4 4
+#> 2 B R 28 2 2
+#> 3 C S 23 >=4 1
+#> 4 D I 20 1 1
+#> 5 E I 27 1 1
+#> 6 F S 20 2 2
+#> 7 G I 25 >=4 <=0.5
+#> 8 H S 25 2 2
+#> 9 I R 20 1 1
+#> 10 J R 22 >=4 <=0.5
mean_amr_distance(y)
#> ℹ Calculating mean AMR distance based on columns "amox", "cipr", "gent",
#> and "tobr"
-#> [1] -0.2236361 -0.0243641 -0.3696267 0.8211210 -0.5440220 -0.4545619
-#> [7] -0.5293690 1.0323298 0.1055421 0.1865871
+#> [1] 0.37458457 0.92678423 0.01430793 -0.80688677 -0.22319302 -0.25784351
+#> [7] -0.08244556 0.15908059 -0.28933760 0.18494914
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 I 17 8 1 -0.3696267 0.00000000
-#> 2 F S 19 1 4 -0.4545619 0.08493517
-#> 3 A I 30 1 <=0.25 -0.2236361 0.14599062
-#> 4 G I 27 <=0.5 <=0.25 -0.5293690 0.15974230
-#> 5 E S 25 <=0.5 0.5 -0.5440220 0.17439527
-#> 6 B S 23 1 16 -0.0243641 0.34526264
-#> 7 I R 18 2 4 0.1055421 0.47516879
-#> 8 J R 20 8 0.5 0.1865871 0.55621386
-#> 9 D R 20 >=16 16 0.8211210 1.19074770
-#> 10 H R 26 8 16 1.0323298 1.40195650
+#> id amox cipr gent tobr amr_distance check_id_C
+#> 1 C S 23 >=4 1 0.01430793 0.0000000
+#> 2 G I 25 >=4 <=0.5 -0.08244556 0.0967535
+#> 3 H S 25 2 2 0.15908059 0.1447727
+#> 4 J R 22 >=4 <=0.5 0.18494914 0.1706412
+#> 5 E I 27 1 1 -0.22319302 0.2375010
+#> 6 F S 20 2 2 -0.25784351 0.2721514
+#> 7 I R 20 1 1 -0.28933760 0.3036455
+#> 8 A I 21 >=4 4 0.37458457 0.3602766
+#> 9 D I 20 1 1 -0.80688677 0.8211947
+#> 10 B R 28 2 2 0.92678423 0.9124763
if (require("dplyr")) {
# support for groups
example_isolates %>%
diff --git a/reference/microorganisms.codes.html b/reference/microorganisms.codes.html
index af00bfcb..1c4d1a15 100644
--- a/reference/microorganisms.codes.html
+++ b/reference/microorganisms.codes.html
@@ -10,7 +10,7 @@
AMR (for R)
- 2.0.0.9006
+ 2.0.0.9007