Next, we evaluate the model on the testing data.
-
+
# Make predictions on the testing set
predictions <- fitted_workflow %>%
predict(testing_data) # Generate predictions
@@ -338,11 +373,11 @@ kappa.
It appears we can predict the Gram based on AMR results with a 99.5%
accuracy based on AMR results of aminoglycosides and beta-lactam
antibiotics. The ROC curve looks like this:
-
+
-
+
diff --git a/articles/AMR_with_tidymodels_files/figure-html/unnamed-chunk-7-1.png b/articles/AMR_with_tidymodels_files/figure-html/unnamed-chunk-8-1.png
similarity index 100%
rename from articles/AMR_with_tidymodels_files/figure-html/unnamed-chunk-7-1.png
rename to articles/AMR_with_tidymodels_files/figure-html/unnamed-chunk-8-1.png
diff --git a/articles/EUCAST.html b/articles/EUCAST.html
index fc99b191c..8f5a5dc48 100644
--- a/articles/EUCAST.html
+++ b/articles/EUCAST.html
@@ -31,7 +31,7 @@
AMR (for R)
- 2.1.1.9163
+ 2.1.1.9182
All so-called group generic functions are implemented for the MIC class (such as !, !=, <, >=, exp(), log2()). Some functions of the stats package are also implemented (such as quantile(), median(), fivenum()). Since sd() and var() are non-generic functions, these could not be extended. Use mad() as an alternative, or use e.g. sd(as.numeric(x)) where x is your vector of MIC values.
Using as.double() or as.numeric() on MIC values will remove the operators and return a numeric vector. Do not use as.integer() on MIC values as by the R convention on factors, it will return the index of the factor levels (which is often useless for regular users).
-Use droplevels() to drop unused levels. At default, it will return a plain factor. Use droplevels(..., as.mic = TRUE) to maintain the mic class.
+Use droplevels() to drop unused levels. At default, it will return a plain factor. Use droplevels(..., as.mic = TRUE) to maintain the mic class.
With rescale_mic(), existing MIC ranges can be limited to a defined range of MIC values. This can be useful to better compare MIC distributions.
For ggplot2, use one of the scale_*_mic() functions to plot MIC values. They allows custom MIC ranges and to plot intermediate log2 levels for missing MIC values.
NA_mic_ is a missing value of the new mic class, analogous to e.g. base R's NA_character_.
diff --git a/reference/as.mo.html b/reference/as.mo.html
index 37eb79f03..7506799c6 100644
--- a/reference/as.mo.html
+++ b/reference/as.mo.html
@@ -7,7 +7,7 @@
AMR (for R)
- 2.1.1.9163
+ 2.1.1.9182