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Residual analysis in random regressions using SAS and S-PLUS.
S Mazumdar1, A E Begley, P R Houck
1Department of Biostatistics, Graduate School of Public Health, University of Pittsburgh, PA 15261, USA.
Computer Methods and Programs in Biomedicine
|March 27, 1999
Summary
The Random Regression Residual Analysis Program (RRRAP) offers tools for analyzing complex data. It uses SAS and S-PLUS to check model assumptions and identify outliers in random regression residual analysis.
Area of Science:
- Statistics
- Biostatistics
- Quantitative Genetics
Background:
- Random regression models are crucial for analyzing longitudinal data and repeated measures.
- Residual analysis is essential for validating model assumptions and identifying deviations.
- Existing methods may lack comprehensive tools for advanced residual diagnostics.
Purpose of the Study:
- To introduce the Random Regression Residual Analysis Program (RRRAP).
- To provide a software package for performing detailed residual analyses in random regression models.
- To facilitate the assessment of model fit and detection of outliers.
Main Methods:
- Utilizes SAS PROCEDURE MIXED for statistical inference.
- Employs both elementary-level and individual-level residuals.
- Incorporates S-PLUS programs for residual transformation and diagnostic statistics.
Main Results:
- S-PLUS programs offer orthogonalization of correlated residuals.
- Provides statistics and plots for checking model assumptions.
- Enables detection of outlying individuals and assessment of model fitting.
Conclusions:
- RRRAP is a valuable tool for researchers conducting random regression residual analysis.
- The package enhances the ability to validate statistical models and interpret results.
- Facilitates robust analysis through advanced residual diagnostics and outlier detection.