MinLinMo: a minimalist approach to variable selection and linear model prediction
Abstrak
AbstractGenerating prediction models from high dimensional data often result in large models with many predictors. Causal inference for such models can therefore be difficult or even impossible in practice. The stand-alone software package MinLinMo emphasizes small linear prediction models over highest possible predictability with a particular focus on including variables correlated with the outcome, minimal memory usage and speed. MinLinMo is demonstrated on large epigenetic datasets with prediction models for chronological age, gestational age, and birth weight comprising, respectively, 15, 14 and 10 predictors. The parsimonious MinLinMo models perform comparably to established prediction models requiring hundreds of predictors.
Penulis (4)
Jon Bohlin
Siri E. Håberg
Per Magnus
Håkon K. Gjessing
Akses Cepat
- Tahun Terbit
- 2024
- Bahasa
- en
- Total Sitasi
- 3×
- Sumber Database
- CrossRef
- DOI
- 10.1186/s12859-024-06000-4
- Akses
- Open Access ✓