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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
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.