ebrahim.gof 1.0.0

Initial Release

This is the first release of the ebrahim.gof package, implementing the Ebrahim-Farrington goodness-of-fit test for logistic regression models.

Features

• Main Function: ef.gof() - Performs the Ebrahim-Farrington goodness-of-fit test
• Dual Mode Support:
  • Ebrahim-Farrington test with automatic grouping for binary data
  • Original Farrington test for grouped binomial data
• Comprehensive Documentation: Detailed help files and vignette
• Robust Testing: Extensive test suite with edge case handling
• Input Validation: Thorough parameter checking and error messages

Key Capabilities

• Binary Data: Automatic grouping of binary (0/1) responses
• Grouped Data: Support for binomial data with multiple trials
• Flexible Grouping: User-specified number of groups (G)
• Statistical Rigor: Based on Farrington’s (1996) theoretical framework
• Sparse Data: Optimized for sparse and challenging datasets

Advantages over Existing Tests

• Better Power: More sensitive than Hosmer-Lemeshow test
• Simplified Implementation: Easy-to-use interface
• Theoretical Foundation: Rigorous asymptotic properties
• Computational Efficiency: Fast execution for binary data

Technical Details

• Test Statistic: Uses modified Pearson chi-square with correction term
• Distribution: Standard normal under null hypothesis
• Expected Value: G - 2 for grouped binary data
• Variance: 2(G - 2) for grouped binary data

References

• Farrington, C. P. (1996). On Assessing Goodness of Fit of Generalized Linear Models to Sparse Data. Journal of the Royal Statistical Society. Series B (Methodological), 58(2), 349-360.
• Ebrahim, Khaled Ebrahim (2025). Goodness-of-Fits Tests and Calibration Machine Learning Algorithms for Logistic Regression Model with Sparse Data. Master’s Thesis, Alexandria University.

Author

Ebrahim Khaled Ebrahim (Alexandria University) Email: ebrahimkhaled@alexu.edu.eg