docs: update R CMD check badge

overdodactyl · Jan 2, 2024 · 7332fa6 · 7332fa6
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diff --git a/README.Rmd b/README.Rmd
@@ -17,7 +17,7 @@ knitr::opts_chunk$set(
 
 <!-- badges: start -->
 [![Lifecycle: experimental](https://img.shields.io/badge/lifecycle-experimental-orange.svg)](https://www.tidyverse.org/lifecycle/#experimental)
-[![R-CMD-check](https://github.com/overdodactyl/diagnosticSummary/workflows/R-CMD-check/badge.svg)](https://github.com/overdodactyl/diagnosticSummary/actions)
+[![R-CMD-check](https://github.com/overdodactyl/diagnosticSummary/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/overdodactyl/diagnosticSummary/actions/workflows/R-CMD-check.yaml)
 <!-- badges: end -->
 
 `diagnosticSummary` is designed to quickly create diagnostic summaries and reports for binary classification data.

diff --git a/README.md b/README.md
@@ -7,7 +7,7 @@
 
 [![Lifecycle:
 experimental](https://img.shields.io/badge/lifecycle-experimental-orange.svg)](https://www.tidyverse.org/lifecycle/#experimental)
-[![R-CMD-check](https://github.com/overdodactyl/diagnosticSummary/workflows/R-CMD-check/badge.svg)](https://github.com/overdodactyl/diagnosticSummary/actions)
+[![R-CMD-check](https://github.com/overdodactyl/diagnosticSummary/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/overdodactyl/diagnosticSummary/actions/workflows/R-CMD-check.yaml)
 <!-- badges: end -->
 
 `diagnosticSummary` is designed to quickly create diagnostic summaries
@@ -67,20 +67,20 @@ summary(dx_obj, variable = "Overall", show_var = F, show_label = F)
 | LRT+                             | 3.54 (2.66, 4.71)    |
 | LRT-                             | 0.20 (0.13, 0.32)    |
 | Odds Ratio                       | 17.59 (9.12, 33.94)  |
-| F1 Score                         | 75.5% (68.3%, 81.2%) |
-| F2 Score                         | 80.7% (74.2%, 86.3%) |
+| F1 Score                         | 75.5% (68.3%, 81.5%) |
+| F2 Score                         | 80.7% (74.0%, 86.4%) |
 | Prevalence                       | 37.5% (31.7%, 43.7%) |
 | False Negative Rate              | 15.3% (8.8%, 24.0%)  |
 | False Positive Rate              | 23.9% (17.6%, 31.2%) |
 | False Discovery Rate             | 32.0% (23.8%, 41.0%) |
 | AUC PR                           | 0.87                 |
 | Cohen’s Kappa                    | 0.58 (0.48, 0.68)    |
-| Matthews Correlation Coefficient | 59.0% (49.0%, 68.5%) |
-| Balanced Accuracy                | 80.4% (75.7%, 85.0%) |
-| Informedness                     | 60.8% (50.8%, 69.7%) |
-| Markedness                       | 57.2% (47.0%, 66.5%) |
-| G-mean                           | 80.3% (75.7%, 84.7%) |
-| Fowlkes-Mallows Index            | 75.9% (69.1%, 82.1%) |
+| Matthews Correlation Coefficient | 59.0% (48.9%, 68.4%) |
+| Balanced Accuracy                | 80.4% (75.3%, 85.1%) |
+| Informedness                     | 60.8% (50.9%, 70.7%) |
+| Markedness                       | 57.2% (47.4%, 67.2%) |
+| G-mean                           | 80.3% (75.1%, 84.8%) |
+| Fowlkes-Mallows Index            | 75.9% (69.6%, 81.4%) |
 | Brier Score                      | 0.11                 |
 | Pearson’s Chi-squared            | p\<0.01              |
 | Pearson’s Chi-squared            | p\<0.01              |