Dataset with 100 patients showing perfect discrimination between positive and negative cases (AUC = 1.0), useful for testing edge case handling.
Format
A data frame with 100 rows and 3 variables:
- patient_id
Character: Patient identifier (PT001-PT100)
- condition
Factor: "Positive" or "Negative" (50%/50% prevalence)
- perfect_test
Numeric: Test values (80-100 for positive, 0-20 for negative)
Details
Complete separation between classes with no overlap. Positive cases have values uniformly distributed between 80-100, negative cases between 0-20. Tests ability to handle perfect discrimination scenarios.
Examples
data(psychopdaROC_perfect)
psychopdaROC(data = psychopdaROC_perfect, dependentVars = "perfect_test",
classVar = "condition", positiveClass = "Positive",
refVar = "perfect_test")
#> Multiple optimal cutpoints found, applying break_ties.
#> Warning: ci.auc() of a ROC curve with AUC == 1 is always 1-1 and can be misleading.
#> Warning: var() of a ROC curve with AUC == 1 is always 0 and can be misleading.
#> Warning: ci.auc() of a ROC curve with AUC == 1 is always 1-1 and can be misleading.
#> Warning: var() of a ROC curve with AUC == 1 is always 0 and can be misleading.
#>
#> ADVANCED ROC ANALYSIS
#>
#>
#>
#>
#> Procedure Notes
#>
#>
#>
#> The ROC analysis has been completed using the following
#> specifications:
#>
#>
#>
#> Measure Variable(s): perfect_test
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#> Class Variable: condition
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#> Positive Class: Positive
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#>
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#> Method: maximize_metric
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#> All Observed Cutpoints: FALSE
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#> Metric: youden
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#> Direction (relative to cutpoint): >=
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#> Tie Breakers: mean
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#> Metric Tolerance: 0.05
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#>
#>
#> <hr />
#>
#> <div style='padding: 10px; background-color: #f8f9fa; border: 1px
#> solid #dee2e6; border-radius: 4px; margin-bottom: 15px;'>
#>
#> Analysis Status
#>
#> Seed: 123Positive Class: Positive (Prevalence: 44%)Analysis Mode:
#> Basic
#>
#> ROC Analysis Summary
#> ──────────────────────────────────────────────────────────────────────────
#> Variable AUC 95% CI Lower 95% CI Upper p-value
#> ──────────────────────────────────────────────────────────────────────────
#> perfect_test 1.0000000 1.0000000 1.0000000
#> ──────────────────────────────────────────────────────────────────────────
#> Note. AUC 95% confidence intervals computed using the DeLong method.
#>
#>
#> Clinical Interpretation
#> ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> Test Performance Level Clinical Recommendation Detailed Interpretation
#> ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> perfect_test Excellent Suitable for clinical use with appropriate cutpoint The test 'perfect_test' has an AUC of 1.000 indicating excellent discriminatory ability. This test can reliably distinguish between diseased and healthy patients.
#> ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#>
#>
#> OPTIMAL CUTPOINTS AND PERFORMANCE
#>
#> no title
#> ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> Cutpoint Sensitivity Specificity PPV NPV Youden's J AUC Metric Score
#> ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> 55.9117509 100.00000 100.00000 100.00000 100.00000 1.0000000 1.0000000 1.0000000
#> ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#>
#>
#> Area Under the ROC Curve
#> ──────────────────────────────────────────────────────────────────────────
#> Variable AUC 95% CI Lower 95% CI Upper p-value
#> ──────────────────────────────────────────────────────────────────────────
#> perfect_test 1.0000000 1.0000000 1.0000000
#> ──────────────────────────────────────────────────────────────────────────
#> Note. AUC 95% confidence intervals computed using the DeLong method.
#>
