sir <- random_sir(10)
sir
#> Class 'sir'
-#> [1] R R S S I R S R S R
+#> [1] I I R I I I R S S 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] -0.621059 -0.621059 1.449138 -0.621059 -0.621059 -0.621059 1.449138
+#> [8] -0.621059 -0.621059 1.449138
mic <- random_mic(10)
mic
#> Class 'mic'
-#> [1] 0.005 0.25 >=256 <=0.002 1 <=0.002 0.5 1 128
-#> [10] 4
+#> [1] 0.01 8 0.025 2 0.125 0.005 128 0.01 2 1
mean_amr_distance(mic)
-#> [1] -1.07476790 -0.13959336 1.51738462 -1.29380847 0.19180224 -1.29380847
-#> [7] 0.02610444 0.19180224 1.35168682 0.52319783
+#> [1] -1.0080431 0.9684558 -0.7371152 0.5585577 -0.2612383 -1.2129921
+#> [7] 1.7882519 -1.0080431 0.5585577 0.3536087
# equal to the Z-score of their log2:
(log2(mic) - mean(log2(mic))) / sd(log2(mic))
-#> [1] -1.07476790 -0.13959336 1.51738462 -1.29380847 0.19180224 -1.29380847
-#> [7] 0.02610444 0.19180224 1.35168682 0.52319783
+#> [1] -1.0080431 0.9684558 -0.7371152 0.5585577 -0.2612383 -1.2129921
+#> [7] 1.7882519 -1.0080431 0.5585577 0.3536087
disk <- random_disk(10)
disk
#> Class 'disk'
-#> [1] 22 44 8 29 39 44 15 49 11 24
+#> [1] 10 50 31 17 26 49 37 45 28 13
mean_amr_distance(disk)
-#> [1] -0.43795351 1.04435068 -1.38123800 0.03368873 0.70746337 1.04435068
-#> [7] -0.90959576 1.38123800 -1.17910561 -0.30319859
+#> [1] -1.41392483 1.33156027 0.02745485 -0.93346493 -0.31573079 1.26292315
+#> [7] 0.43927762 0.98837464 -0.17845653 -1.20801344
y <- data.frame(
id = LETTERS[1:10],
@@ -236,21 +235,21 @@
)
y
#> id amox cipr gent tobr
-#> 1 A S 20 2 2
-#> 2 B S 23 0.5 0.5
-#> 3 C I 25 0.5 1
-#> 4 D S 19 16 0.5
-#> 5 E I 24 1 2
-#> 6 F I 28 0.5 0.5
-#> 7 G R 31 4 4
-#> 8 H S 22 8 1
-#> 9 I S 21 0.5 0.5
-#> 10 J R 30 0.5 1
+#> 1 A S 19 >=16 <=1
+#> 2 B R 20 8 <=1
+#> 3 C I 20 2 4
+#> 4 D S 19 <=1 8
+#> 5 E I 23 4 4
+#> 6 F I 31 <=1 8
+#> 7 G R 18 2 4
+#> 8 H I 22 2 <=1
+#> 9 I I 22 <=1 <=1
+#> 10 J I 25 >=16 2
mean_amr_distance(y)
#> ℹ Calculating mean AMR distance based on columns "amox", "cipr", "gent",
#> and "tobr"
-#> [1] -0.07375917 -0.63113176 -0.27390329 -0.21345353 0.03479741 -0.33098765
-#> [7] 1.54821445 0.07224506 -0.75118940 0.61916787
+#> [1] -0.1865874 0.3129310 -0.1974612 -0.2212515 0.1561783 0.5594410
+#> [7] 0.2653504 -0.4668135 -0.6252799 0.4034927
y$amr_distance <- mean_amr_distance(y, where(is.mic))
#> Error in .subset(x, j): invalid subscript type 'list'
y[order(y$amr_distance), ]
@@ -267,16 +266,16 @@
#> 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 I 25 0.5 1 -0.27390329 0.00000000
-#> 2 F I 28 0.5 0.5 -0.33098765 0.05708436
-#> 3 D S 19 16 0.5 -0.21345353 0.06044977
-#> 4 A S 20 2 2 -0.07375917 0.20014413
-#> 5 E I 24 1 2 0.03479741 0.30870071
-#> 6 H S 22 8 1 0.07224506 0.34614835
-#> 7 B S 23 0.5 0.5 -0.63113176 0.35722847
-#> 8 I S 21 0.5 0.5 -0.75118940 0.47728611
-#> 9 J R 30 0.5 1 0.61916787 0.89307117
-#> 10 G R 31 4 4 1.54821445 1.82211774
+#> 1 C I 20 2 4 -0.1974612 0.00000000
+#> 2 A S 19 >=16 <=1 -0.1865874 0.01087383
+#> 3 D S 19 <=1 8 -0.2212515 0.02379027
+#> 4 H I 22 2 <=1 -0.4668135 0.26935231
+#> 5 E I 23 4 4 0.1561783 0.35363955
+#> 6 I I 22 <=1 <=1 -0.6252799 0.42781873
+#> 7 G R 18 2 4 0.2653504 0.46281164
+#> 8 B R 20 8 <=1 0.3129310 0.51039218
+#> 9 J I 25 >=16 2 0.4034927 0.60095395
+#> 10 F I 31 <=1 8 0.5594410 0.75690225
if (require("dplyr")) {
# support for groups
example_isolates %>%
diff --git a/reference/microorganisms.codes.html b/reference/microorganisms.codes.html
index 581a669a..38b34779 100644
--- a/reference/microorganisms.codes.html
+++ b/reference/microorganisms.codes.html
@@ -1,5 +1,5 @@
-Data Set with 5 754 Common Microorganism Codes — microorganisms.codes • AMR (for R)Data Set with 5 754 Common Microorganism Codes — microorganisms.codes • AMR (for R)
@@ -10,7 +10,7 @@
AMR (for R)
- 2.0.0.9013
+ 2.0.0.9014