A review of spline function procedures in R.
Aris Perperoglou1, Willi Sauerbrei2, Michal Abrahamowicz3
1Department of Mathematical Sciences, University of Essex, Colchester, UK. aperpe@essex.ac.uk.
BMC Medical Research Methodology
|March 8, 2019
Summary
Spline modeling in R offers powerful regression analysis tools. Careful hyper-parameter selection is key for accurate results, especially for analysts needing guidance on these statistical methods.
Area of Science:
- Statistical modeling
- Computational statistics
Background:
- Spline modeling is an established statistical regression analysis tool.
- User-friendly software packages are crucial for spline modeling.
- This article provides guidance on spline techniques in R for observational research.
Purpose of the Study:
- To provide an overview of widely used spline-based techniques.
- To demonstrate their implementation in R.
- To guide analysts in using spline modeling effectively.
Main Methods:
- Focus on R Language for Statistical Computing.
- Identified R packages for spline modeling in regression.
- Used simulated and real data for analysis.
Main Results:
- Presented scenarios with univariate data using different basis functions.
- Demonstrated that different R packages yield varying results even for simple data.
- Highlighted the impact of hyper-parameter choices on outcomes.
Conclusions:
- Spline modeling presents challenges for analysts, with hyper-parameters being a major factor.
- Experienced users can achieve reasonable outcomes, but guidance is needed for many analysts.
- Effective use of spline modeling requires understanding and careful parameter selection.
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