sir <- random_sir(10)
sir
#> Class 'sir'
-#> [1] R R I I I I R S S S
+#> [1] S R S R R R I I S S
mean_amr_distance(sir)
-#> [1] 1.449138 1.449138 -0.621059 -0.621059 -0.621059 -0.621059 1.449138
-#> [8] -0.621059 -0.621059 -0.621059
+#> [1] -0.7745967 1.1618950 -0.7745967 1.1618950 1.1618950 1.1618950
+#> [7] -0.7745967 -0.7745967 -0.7745967 -0.7745967
mic <- random_mic(10)
mic
#> Class 'mic'
-#> [1] >=128 32 0.25 2 0.5 0.002 0.025 64 8 64
+#> [1] 0.005 0.002 0.025 1 64 0.0625 0.025 0.001 16 0.25
mean_amr_distance(mic)
-#> [1] 1.10682807 0.73345287 -0.57336033 -0.01329753 -0.38667273 -1.87378587
-#> [7] -1.19352311 0.92014047 0.36007767 0.92014047
+#> [1] -0.8096905 -1.0573468 -0.3746895 0.6223458 1.7464143 -0.1270332
+#> [7] -0.3746895 -1.2446916 1.3717248 0.2476563
# equal to the Z-score of their log2:
(log2(mic) - mean(log2(mic))) / sd(log2(mic))
-#> [1] 1.10682807 0.73345287 -0.57336033 -0.01329753 -0.38667273 -1.87378587
-#> [7] -1.19352311 0.92014047 0.36007767 0.92014047
+#> [1] -0.8096905 -1.0573468 -0.3746895 0.6223458 1.7464143 -0.1270332
+#> [7] -0.3746895 -1.2446916 1.3717248 0.2476563
disk <- random_disk(10)
disk
#> Class 'disk'
-#> [1] 40 43 7 33 17 6 43 34 8 46
+#> [1] 10 22 14 48 42 42 49 25 10 19
mean_amr_distance(disk)
-#> [1] 0.7494646 0.9322608 -1.2612940 0.3229400 -0.6519732 -1.3222261
-#> [7] 0.9322608 0.3838721 -1.2003619 1.1150570
+#> [1] -1.1569176 -0.3899004 -0.9012452 1.2719702 0.8884616 0.8884616
+#> [7] 1.3358883 -0.1981461 -1.1569176 -0.5816547
y <- data.frame(
id = LETTERS[1:10],
@@ -234,22 +234,22 @@
tobr = random_mic(10, ab = "tobr", mo = "Escherichia coli")
)
y
-#> id amox cipr gent tobr
-#> 1 A I 29 <=2 2
-#> 2 B R 21 >=8 2
-#> 3 C R 31 4 1
-#> 4 D S 19 4 1
-#> 5 E I 21 4 1
-#> 6 F R 31 <=2 4
-#> 7 G S 20 4 1
-#> 8 H R 25 4 2
-#> 9 I R 24 >=8 4
-#> 10 J S 20 >=8 2
+#> id amox cipr gent tobr
+#> 1 A R 26 0.5 4
+#> 2 B S 17 <=0.25 2
+#> 3 C R 27 1 1
+#> 4 D I 17 >=2 2
+#> 5 E I 20 0.5 <=0.5
+#> 6 F I 29 <=0.25 2
+#> 7 G R 20 0.5 4
+#> 8 H S 20 >=2 2
+#> 9 I R 27 0.5 <=0.5
+#> 10 J R 17 0.5 4
mean_amr_distance(y)
#> ℹ Calculating mean AMR distance based on columns "amox", "cipr", "gent" and
#> "tobr"
-#> [1] -0.2859019 0.4406350 0.3166812 -0.7958139 -0.6894550 0.6117315
-#> [7] -0.7426344 0.3145374 0.9171062 -0.0868861
+#> [1] 0.64170105 -0.76377032 0.49004949 -0.05578894 -0.80988224 -0.13053253
+#> [7] 0.32508215 0.10252050 0.03384812 0.16677271
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 +265,17 @@
}
#> 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 R 31 4 1 0.3166812 0.000000000
-#> 2 H R 25 4 2 0.3145374 0.002143877
-#> 3 B R 21 >=8 2 0.4406350 0.123953772
-#> 4 F R 31 <=2 4 0.6117315 0.295050227
-#> 5 J S 20 >=8 2 -0.0868861 0.403567331
-#> 6 I R 24 >=8 4 0.9171062 0.600424979
-#> 7 A I 29 <=2 2 -0.2859019 0.602583175
-#> 8 E I 21 4 1 -0.6894550 1.006136186
-#> 9 G S 20 4 1 -0.7426344 1.059315640
-#> 10 D S 19 4 1 -0.7958139 1.112495094
+#> id amox cipr gent tobr amr_distance check_id_C
+#> 1 C R 27 1 1 0.49004949 0.0000000
+#> 2 A R 26 0.5 4 0.64170105 0.1516516
+#> 3 G R 20 0.5 4 0.32508215 0.1649673
+#> 4 J R 17 0.5 4 0.16677271 0.3232768
+#> 5 H S 20 >=2 2 0.10252050 0.3875290
+#> 6 I R 27 0.5 <=0.5 0.03384812 0.4562014
+#> 7 D I 17 >=2 2 -0.05578894 0.5458384
+#> 8 F I 29 <=0.25 2 -0.13053253 0.6205820
+#> 9 B S 17 <=0.25 2 -0.76377032 1.2538198
+#> 10 E I 20 0.5 <=0.5 -0.80988224 1.2999317
if (require("dplyr")) {
# support for groups
example_isolates %>%
diff --git a/reference/microorganisms.codes.html b/reference/microorganisms.codes.html
index c53622eb..d6639ce2 100644
--- a/reference/microorganisms.codes.html
+++ b/reference/microorganisms.codes.html
@@ -10,7 +10,7 @@
AMR (for R)
- 1.8.2.9131
+ 1.8.2.9132