docs: update README.Rmd

Ahmad-Alsaleh · Jun 17, 2024 · 619f6c9 · 619f6c9
1 parent b1d430e
commit 619f6c9
Show file tree

Hide file tree

Showing 2 changed files with 35 additions and 30 deletions.
diff --git a/README.Rmd b/README.Rmd
@@ -22,37 +22,29 @@ knitr::opts_chunk$set(
 [![Codecov test coverage](https://codecov.io/gh/Ahmad-Alsaleh/EvaluateFeatureSelection/branch/main/graph/badge.svg)](https://app.codecov.io/gh/Ahmad-Alsaleh/EvaluateFeatureSelection?branch=main)
 <!-- badges: end -->
 
-The goal of EvaluateFeatureSelection is to ...
+Generates plots to visualize and assess the performance of feature selection methods using supervised learning.
+    It also provides functions to plot scree plots to visualize good cutting points for the number of features to be selected.
 
 ## Installation
 
-You can install the development version of EvaluateFeatureSelection like so:
+You can install the development version of EvaluateFeatureSelection like
+so:
 
-``` r
-# FILL THIS IN! HOW CAN PEOPLE INSTALL YOUR DEV PACKAGE?
+```r
+install.packages("remotes")
+remotes::install_github("Ahmad-Alsaleh/EvaluateFeatureSelection")
 ```
 
 ## Example
 
-This is a basic example which shows you how to solve a common problem:
-
-```{r example}
+Generate a scree plot
+```r
 library(EvaluateFeatureSelection)
-## basic example code
-```
-
-What is special about using `README.Rmd` instead of just `README.md`? You can include R chunks like so:
-
-```{r cars}
-summary(cars)
+features_scores <- c(x1 = 0.8165005, x2 = -0.1178857, ...)
+get_scree_plot(features_scores)
 ```
+![BAM Scores Scree Plot](https://github.com/Ahmad-Alsaleh/EvaluateFeatureSelection/assets/61240880/46da58ea-c7d0-4247-8d8b-af6758d2ff18)
 
-You'll still need to render `README.Rmd` regularly, to keep `README.md` up-to-date. `devtools::build_readme()` is handy for this.
-
-You can also embed plots, for example:
-
-```{r pressure, echo = FALSE}
-plot(pressure)
-```
+Similarly, you can use `get_auc_plot(...)` or `get_acc_plot(...)` to evaluate the performance of feature selection methods using supervised learning and AUC/accuracy as the performance metric.
 
-In that case, don't forget to commit and push the resulting figure files, so they display on GitHub and CRAN.
+![image](https://github.com/Ahmad-Alsaleh/EvaluateFeatureSelection/assets/61240880/5684b533-ae91-491e-8584-9f356a909a20)
diff --git a/README.md b/README.md
@@ -14,8 +14,10 @@ status](https://www.r-pkg.org/badges/version/EvaluateFeatureSelection)](https://
 coverage](https://codecov.io/gh/Ahmad-Alsaleh/EvaluateFeatureSelection/branch/main/graph/badge.svg)](https://app.codecov.io/gh/Ahmad-Alsaleh/EvaluateFeatureSelection?branch=main)
 <!-- badges: end -->
 
-This package generates plots to visualize and assess the performance of feature selection methods using supervised learning.
-    It also provides functions to plot scree plots to visualize good cutting points for the number of features to be selected.
+Generates plots to visualize and assess the performance of feature
+selection methods using supervised learning. It also provides functions
+to plot scree plots to visualize good cutting points for the number of
+features to be selected.
 
 ## Installation
 
@@ -36,10 +38,21 @@ library(EvaluateFeatureSelection)
 features_scores <- c(x1 = 0.8165005, x2 = -0.1178857, ...)
 get_scree_plot(features_scores)
 ```
-![BAM Scores Scree Plot](https://github.com/Ahmad-Alsaleh/EvaluateFeatureSelection/assets/61240880/46da58ea-c7d0-4247-8d8b-af6758d2ff18)
-
-
-Similarly, you can use `get_auc_plot(...)` to evaluate the performance of feature selection methods using supervised learning.
-
-![image](https://github.com/Ahmad-Alsaleh/EvaluateFeatureSelection/assets/61240880/5684b533-ae91-491e-8584-9f356a909a20)
 
+<figure>
+<img
+src="https://github.com/Ahmad-Alsaleh/EvaluateFeatureSelection/assets/61240880/46da58ea-c7d0-4247-8d8b-af6758d2ff18"
+alt="BAM Scores Scree Plot" />
+<figcaption aria-hidden="true">BAM Scores Scree Plot</figcaption>
+</figure>
+
+Similarly, you can use `get_auc_plot(...)` or `get_acc_plot(...)` to
+evaluate the performance of feature selection methods using supervised
+learning and AUC/accuracy as the performance metric.
+
+<figure>
+<img
+src="https://github.com/Ahmad-Alsaleh/EvaluateFeatureSelection/assets/61240880/5684b533-ae91-491e-8584-9f356a909a20"
+alt="image" />
+<figcaption aria-hidden="true">image</figcaption>
+</figure>