sir <- random_sir(10)
sir
#> Class 'sir'
-#> [1] I R I I R R R I R I
+#> [1] R I S R R I I S I S
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] 1.449138 -0.621059 -0.621059 1.449138 1.449138 -0.621059 -0.621059
+#> [8] -0.621059 -0.621059 -0.621059
mic <- random_mic(10)
mic
#> Class 'mic'
-#> [1] 256 0.01 4 64 8 1 1 0.002 0.125 256
+#> [1] 0.5 64 128 2 64 0.5 4 16 <=0.01 64
mean_amr_distance(mic)
-#> [1] 1.2331707 -1.2886091 0.1999266 0.8887560 0.3721339 -0.1444882
-#> [7] -0.1444882 -1.6884622 -0.6611102 1.2331707
+#> [1] -0.76841729 0.84415551 1.07452305 -0.30768220 0.84415551 -0.76841729
+#> [7] -0.07731466 0.38342042 -2.06857856 0.84415551
# equal to the Z-score of their log2:
(log2(mic) - mean(log2(mic))) / sd(log2(mic))
-#> [1] 1.2331707 -1.2886091 0.1999266 0.8887560 0.3721339 -0.1444882
-#> [7] -0.1444882 -1.6884622 -0.6611102 1.2331707
+#> [1] -0.76841729 0.84415551 1.07452305 -0.30768220 0.84415551 -0.76841729
+#> [7] -0.07731466 0.38342042 -2.06857856 0.84415551
disk <- random_disk(10)
disk
#> Class 'disk'
-#> [1] 25 6 45 23 39 45 20 49 8 43
+#> [1] 33 13 32 20 13 17 12 43 11 48
mean_amr_distance(disk)
-#> [1] -0.3319640 -1.5220235 0.9207303 -0.4572334 0.5449220 0.9207303
-#> [7] -0.6451375 1.1712691 -1.3967541 0.7954608
+#> [1] 0.6399929 -0.8145365 0.5672665 -0.3054512 -0.8145365 -0.5236306
+#> [7] -0.8872630 1.3672577 -0.9599894 1.7308900
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 R 27 1 0.5
-#> 2 B R 29 1 0.5
-#> 3 C I 19 <=0.5 4
-#> 4 D I 27 1 <=0.25
-#> 5 E S 30 <=0.5 4
-#> 6 F I 22 1 1
-#> 7 G I 26 1 1
-#> 8 H R 22 1 2
-#> 9 I I 21 4 0.5
-#> 10 J R 29 1 <=0.25
+#> id amox cipr gent tobr
+#> 1 A S 29 2 4
+#> 2 B R 18 1 1
+#> 3 C S 22 1 1
+#> 4 D I 22 2 <=0.5
+#> 5 E S 29 4 1
+#> 6 F S 19 2 2
+#> 7 G R 19 2 <=0.5
+#> 8 H R 23 4 4
+#> 9 I S 22 2 4
+#> 10 J R 31 1 2
mean_amr_distance(y)
#> ℹ Calculating mean AMR distance based on columns "amox", "cipr", "gent",
#> and "tobr"
-#> [1] 0.27087972 0.39969323 -0.52646026 -0.38265093 0.18201405 -0.36586924
-#> [7] -0.10824222 0.28766141 0.01268871 0.23028550
+#> [1] 0.43964438 -0.43272533 -0.70168251 -0.57585273 0.35248847 -0.31125566
+#> [7] -0.25310412 0.93983415 0.06310433 0.47954902
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 19 <=0.5 4 -0.52646026 0.0000000
-#> 2 D I 27 1 <=0.25 -0.38265093 0.1438093
-#> 3 F I 22 1 1 -0.36586924 0.1605910
-#> 4 G I 26 1 1 -0.10824222 0.4182180
-#> 5 I I 21 4 0.5 0.01268871 0.5391490
-#> 6 E S 30 <=0.5 4 0.18201405 0.7084743
-#> 7 J R 29 1 <=0.25 0.23028550 0.7567458
-#> 8 A R 27 1 0.5 0.27087972 0.7973400
-#> 9 H R 22 1 2 0.28766141 0.8141217
-#> 10 B R 29 1 0.5 0.39969323 0.9261535
+#> id amox cipr gent tobr amr_distance check_id_C
+#> 1 C S 22 1 1 -0.70168251 0.0000000
+#> 2 D I 22 2 <=0.5 -0.57585273 0.1258298
+#> 3 B R 18 1 1 -0.43272533 0.2689572
+#> 4 F S 19 2 2 -0.31125566 0.3904268
+#> 5 G R 19 2 <=0.5 -0.25310412 0.4485784
+#> 6 I S 22 2 4 0.06310433 0.7647868
+#> 7 E S 29 4 1 0.35248847 1.0541710
+#> 8 A S 29 2 4 0.43964438 1.1413269
+#> 9 J R 31 1 2 0.47954902 1.1812315
+#> 10 H R 23 4 4 0.93983415 1.6415167
if (require("dplyr")) {
# support for groups
example_isolates %>%
diff --git a/reference/microorganisms.codes.html b/reference/microorganisms.codes.html
index 9f767790..63fe948f 100644
--- a/reference/microorganisms.codes.html
+++ b/reference/microorganisms.codes.html
@@ -10,7 +10,7 @@
AMR (for R)
- 2.0.0.9024
+ 2.0.0.9025