sir <- random_sir(10)
sir
#> Class 'sir'
-#> [1] R R S R S I R I R S
+#> [1] S R S S S R R R R R
mean_amr_distance(sir)
-#> [1] 0.9486833 0.9486833 -0.9486833 0.9486833 -0.9486833 -0.9486833
-#> [7] 0.9486833 -0.9486833 0.9486833 -0.9486833
+#> [1] -1.1618950 0.7745967 -1.1618950 -1.1618950 -1.1618950 0.7745967
+#> [7] 0.7745967 0.7745967 0.7745967 0.7745967
mic <- random_mic(10)
mic
#> Class 'mic'
-#> [1] 1 0.125 2 0.005 0.5 64 0.002 128 2 2
+#> [1] 0.005 0.25 1 2 >=16 8 1 1 0.025 4
mean_amr_distance(mic)
-#> [1] 0.0902697 -0.4972145 0.2860978 -1.4066119 -0.1055584 1.2652381
-#> [7] -1.6654825 1.4610662 0.2860978 0.2860978
+#> [1] -1.9714967 -0.4147020 0.1369756 0.4128144 1.2403309 0.9644921
+#> [7] 0.1369756 0.1369756 -1.3310188 0.6886533
# equal to the Z-score of their log2:
(log2(mic) - mean(log2(mic))) / sd(log2(mic))
-#> [1] 0.0902697 -0.4972145 0.2860978 -1.4066119 -0.1055584 1.2652381
-#> [7] -1.6654825 1.4610662 0.2860978 0.2860978
+#> [1] -1.9714967 -0.4147020 0.1369756 0.4128144 1.2403309 0.9644921
+#> [7] 0.1369756 0.1369756 -1.3310188 0.6886533
disk <- random_disk(10)
disk
#> Class 'disk'
-#> [1] 48 19 18 10 16 48 41 6 26 24
+#> [1] 15 15 50 41 10 21 44 42 10 41
mean_amr_distance(disk)
-#> [1] 1.47901421 -0.43578097 -0.50180839 -1.03002776 -0.63386323 1.47901421
-#> [7] 1.01682227 -1.29413744 0.02641097 -0.10564387
+#> [1] -0.8693350 -0.8693350 1.3196381 0.7567593 -1.1820455 -0.4940825
+#> [7] 0.9443855 0.8193014 -1.1820455 0.7567593
y <- data.frame(
id = LETTERS[1:10],
@@ -143,21 +143,21 @@
)
y
#> id amox cipr gent tobr
-#> 1 A S 22 <=0.25 4
-#> 2 B I 24 4 2
-#> 3 C S 21 4 4
-#> 4 D I 18 1 2
-#> 5 E R 26 4 2
-#> 6 F R 22 0.5 1
-#> 7 G I 23 4 4
-#> 8 H S 23 2 1
-#> 9 I S 27 <=0.25 1
-#> 10 J R 25 4 2
+#> 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
mean_amr_distance(y)
#> ℹ Calculating mean AMR distance based on columns "amox", "cipr", "gent",
#> and "tobr"
-#> [1] -0.3343139 0.1355870 0.1534539 -0.7329889 0.8453491 -0.2831686
-#> [7] 0.3456668 -0.4126742 -0.4661540 0.7492427
+#> [1] -0.04047504 -0.25789134 -0.08826444 -0.63237868 -0.42488867 0.02453739
+#> [7] 1.18267023 0.72772870 0.36605356 -0.85709171
y$amr_distance <- mean_amr_distance(y, where(is.mic))
#> Error in .subset(x, j): invalid subscript type 'list'
y[order(y$amr_distance), ]
@@ -174,16 +174,16 @@
#> 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 21 4 4 0.1534539 0.00000000
-#> 2 B I 24 4 2 0.1355870 0.01786681
-#> 3 G I 23 4 4 0.3456668 0.19221294
-#> 4 F R 22 0.5 1 -0.2831686 0.43662245
-#> 5 A S 22 <=0.25 4 -0.3343139 0.48776774
-#> 6 H S 23 2 1 -0.4126742 0.56612805
-#> 7 J R 25 4 2 0.7492427 0.59578882
-#> 8 I S 27 <=0.25 1 -0.4661540 0.61960784
-#> 9 E R 26 4 2 0.8453491 0.69189529
-#> 10 D I 18 1 2 -0.7329889 0.88644273
+#> 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
if (require("dplyr")) {
# support for groups
example_isolates %>%
diff --git a/reference/microorganisms.codes.html b/reference/microorganisms.codes.html
index 07fa8e899..ecb4b4d38 100644
--- a/reference/microorganisms.codes.html
+++ b/reference/microorganisms.codes.html
@@ -7,7 +7,7 @@
AMR (for R)
- 2.1.1.9149
+ 2.1.1.9150