Overview

Installation

install.packages("ROCnGO")

# install.packages("devtools")devtools::install_github("pabloPNC/ROCnGO")

Usage

library(ROCnGO)# Iris subsetiris_subset<-iris[iris$Species!="versicolor", ]# Select Species = "virginica" as the condition of interestiris_subset$Species<- relevel(iris_subset$Species,"virginica")# Summarize a predictor over high sensitivity regionsummarize_predictor(iris_subset,predictor=Sepal.Length,response=Species,threshold=0.9,ratio="tpr")#> ℹ Upper threshold 1 already included in points.#> • Skipping upper threshold interpolation#> # A tibble: 1 × 5#>     auc   pauc np_auc fp_auc curve_shape#>   <dbl>  <dbl>  <dbl>  <dbl> <chr>#> 1 0.985 0.0847  0.847  0.852 Concave

# Summarize several predictors simultaneouslysummarize_dataset(iris_subset,predictors= c(Sepal.Length,Sepal.Width,Petal.Length,Petal.Width),response=Species,threshold=0.9,ratio="tpr")#> ℹ Lower 0.9 and upper 1 thresholds already included in points#> • Skipping lower and upper threshold interpolation#> $data#> # A tibble: 4 × 6#>   identifier     auc   pauc np_auc fp_auc curve_shape#>   <chr>        <dbl>  <dbl>  <dbl>  <dbl> <chr>#> 1 Sepal.Length 0.985 0.0847 0.847   0.852 Concave#> 2 Sepal.Width  0.166 0.0016 0.0160  0.9   Hook under chance#> 3 Petal.Length 1     0.1    1       1     Concave#> 4 Petal.Width  1     0.1    1       1     Concave#>#> $curve_shape#> # A tibble: 2 × 2#>   curve_shape       count#>   <chr>             <int>#> 1 Concave               3#> 2 Hook under chance     1#>#> $auc#> # A tibble: 2 × 3#> # Groups:   auc > 0.5 [2]#>   `auc > 0.5` `auc > 0.8` count#>   <lgl>       <lgl>       <int>#> 1 FALSE       FALSE           1#> 2 TRUE        TRUE            3

# Plot ROC curve of classifiersplot_roc_curve(iris_subset,predictor=Sepal.Length,response=Species)+  add_roc_curve(iris_subset,predictor=Petal.Length,response=Species)+  add_roc_points(iris_subset,predictor=Sepal.Width,response=Species)+  add_chance_line()