sir <- random_sir(10)
sir
#> Class 'sir'
-#> [1] S S I I I S S S R R
+#> [1] R R S R S I R I R S
mean_amr_distance(sir)
-#> [1] -0.4743416 -0.4743416 -0.4743416 -0.4743416 -0.4743416 -0.4743416
-#> [7] -0.4743416 -0.4743416 1.8973666 1.8973666
+#> [1] 0.9486833 0.9486833 -0.9486833 0.9486833 -0.9486833 -0.9486833
+#> [7] 0.9486833 -0.9486833 0.9486833 -0.9486833
mic <- random_mic(10)
mic
#> Class 'mic'
-#> [1] 0.025 1 0.001 8 0.5 16 64 128 4 64
+#> [1] 1 0.125 2 0.005 0.5 64 0.002 128 2 2
mean_amr_distance(mic)
-#> [1] -1.1967905 -0.2158758 -2.0527259 0.3370712 -0.4001915 0.5213868
-#> [7] 0.8900182 1.0743338 0.1527555 0.8900182
+#> [1] 0.0902697 -0.4972145 0.2860978 -1.4066119 -0.1055584 1.2652381
+#> [7] -1.6654825 1.4610662 0.2860978 0.2860978
# equal to the Z-score of their log2:
(log2(mic) - mean(log2(mic))) / sd(log2(mic))
-#> [1] -1.1967905 -0.2158758 -2.0527259 0.3370712 -0.4001915 0.5213868
-#> [7] 0.8900182 1.0743338 0.1527555 0.8900182
+#> [1] 0.0902697 -0.4972145 0.2860978 -1.4066119 -0.1055584 1.2652381
+#> [7] -1.6654825 1.4610662 0.2860978 0.2860978
disk <- random_disk(10)
disk
#> Class 'disk'
-#> [1] 42 8 24 15 32 45 46 42 49 14
+#> [1] 48 19 18 10 16 48 41 6 26 24
mean_amr_distance(disk)
-#> [1] 0.67331608 -1.54928069 -0.50335280 -1.09168723 0.01961115 0.86942756
-#> [7] 0.93479805 0.67331608 1.13090953 -1.15705773
+#> [1] 1.47901421 -0.43578097 -0.50180839 -1.03002776 -0.63386323 1.47901421
+#> [7] 1.01682227 -1.29413744 0.02641097 -0.10564387
y <- data.frame(
id = LETTERS[1:10],
@@ -142,22 +142,22 @@
tobr = random_mic(10, ab = "tobr", mo = "Escherichia coli")
)
y
-#> id amox cipr gent tobr
-#> 1 A R 27 8 0.5
-#> 2 B I 21 8 4
-#> 3 C S 22 2 1
-#> 4 D R 22 4 4
-#> 5 E S 19 8 0.5
-#> 6 F S 30 2 2
-#> 7 G R 20 16 4
-#> 8 H R 27 4 2
-#> 9 I S 20 1 1
-#> 10 J R 17 16 4
+#> 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
mean_amr_distance(y)
#> ℹ Calculating mean AMR distance based on columns "amox", "cipr", "gent",
#> and "tobr"
-#> [1] 0.27346725 0.04720942 -0.67302993 0.39497313 -0.68358519 0.01305317
-#> [7] 0.64812902 0.49329507 -0.98062442 0.46711247
+#> [1] -0.3343139 0.1355870 0.1534539 -0.7329889 0.8453491 -0.2831686
+#> [7] 0.3456668 -0.4126742 -0.4661540 0.7492427
y$amr_distance <- mean_amr_distance(y, where(is.mic))
#> Error in .subset(x, j): invalid subscript type 'list'
y[order(y$amr_distance), ]
@@ -173,17 +173,17 @@
}
#> 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 S 22 2 1 -0.67302993 0.00000000
-#> 2 E S 19 8 0.5 -0.68358519 0.01055526
-#> 3 I S 20 1 1 -0.98062442 0.30759449
-#> 4 F S 30 2 2 0.01305317 0.68608310
-#> 5 B I 21 8 4 0.04720942 0.72023935
-#> 6 A R 27 8 0.5 0.27346725 0.94649718
-#> 7 D R 22 4 4 0.39497313 1.06800305
-#> 8 J R 17 16 4 0.46711247 1.14014240
-#> 9 H R 27 4 2 0.49329507 1.16632499
-#> 10 G R 20 16 4 0.64812902 1.32115894
+#> 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
if (require("dplyr")) {
# support for groups
example_isolates %>%
diff --git a/reference/microorganisms.codes.html b/reference/microorganisms.codes.html
index 6ed7be353..083b506cf 100644
--- a/reference/microorganisms.codes.html
+++ b/reference/microorganisms.codes.html
@@ -7,7 +7,7 @@
AMR (for R)
- 2.1.1.9146
+ 2.1.1.9147
@@ -285,7 +286,7 @@ Especially the scale_*_mic() functions are relevant wrappers to plot MIC values
theme_sir() is a [ggplot2 theme][ggplot2::theme() with minimal distraction.
labels_sir_count() print datalabels on the bars with percentage and number of isolates, using ggplot2::geom_text().
The interpretation of "I" will be named "Increased exposure" for all EUCAST guidelines since 2019, and will be named "Intermediate" in all other cases.
-
For interpreting MIC values as well as disk diffusion diameters, supported guidelines to be used as input for the guideline argument are: "EUCAST 2024", "EUCAST 2023", "EUCAST 2022", "EUCAST 2021", "EUCAST 2020", "EUCAST 2019", "EUCAST 2018", "EUCAST 2017", "EUCAST 2016", "EUCAST 2015", "EUCAST 2014", "EUCAST 2013", "EUCAST 2012", "EUCAST 2011", "CLSI 2024", "CLSI 2023", "CLSI 2022", "CLSI 2021", "CLSI 2020", "CLSI 2019", "CLSI 2018", "CLSI 2017", "CLSI 2016", "CLSI 2015", "CLSI 2014", "CLSI 2013", "CLSI 2012", and "CLSI 2011". Simply using "CLSI" or "EUCAST" as input will automatically select the latest version of that guideline.
+
For interpreting MIC values as well as disk diffusion diameters, the default guideline is EUCAST 2024, unless the package option AMR_guideline is set. See as.sir() for more information.