diagnosticSummary is designed to quickly create diagnostic summaries
and reports for binary classification data.
You can install the development version from GitHub with:
# install.packages("devtools")
devtools::install_github("overdodactyl/diagnosticSummary")library(diagnosticSummary)
# Load sample data
data("dx_heart_failure")
head(dx_heart_failure)
#> AgeGroup Sex truth predicted AgeSex
#> 1 (20,50] Male 0 0.016164112 (20,50] - Male
#> 2 (20,50] Male 0 0.074193671 (20,50] - Male
#> 3 (20,50] Female 0 0.004677979 (20,50] - Female
#> 4 (20,50] Female 0 0.017567313 (20,50] - Female
#> 5 (20,50] Female 0 0.017517025 (20,50] - Female
#> 6 (20,50] Male 0 0.051570734 (20,50] - Male
# Create dx object
dx_obj <- dx(
data = dx_heart_failure,
true_varname = "truth",
pred_varname = "predicted",
outcome_label = "Heart Attack",
threshold_range = c(.1,.2,.3),
setthreshold = .3,
doboot = TRUE,
bootreps = 1000,
grouping_variables = c("AgeGroup", "Sex", "AgeSex")
)summary(dx_obj, variable = "Overall", show_var = F, show_label = F)| measure | summary |
|---|---|
| AUC ROC | 0.904 (0.864, 0.943) |
| Accuracy | 79.3% (73.9%, 84.1%) |
| Sensitivity | 84.7% (76.0%, 91.2%) |
| Specificity | 76.1% (68.8%, 82.4%) |
| Positive Predictive Value | 68.0% (59.0%, 76.2%) |
| Negative Predictive Value | 89.2% (82.8%, 93.8%) |
| 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%, 81.5%) |
| 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% (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 |
| Fisher’s Exact | p<0.01 |
| G-Test | p<0.01 |
Threshold= 0.3