Research map / Probabilistic and scientific modeling / Molecules and proteins
LMLFM: Longitudinal Multi-Level Factorization Machine
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use LMLFM's multi-level factorization and hierarchical sparsity model | Longitudinal prediction with simulated multi-level correlation · Simulated data, p=100 | R² (%): 90$\pm$1 -> 92$\pm$1 (M-LMM); 88$\pm$2 -> 92$\pm$1 (rPQL) | Helped |
| Use LMLFM instead of LASSO | High-dimensional longitudinal prediction · Simulated data, p=5000 | R² (%): 84$\pm$4 -> 88$\pm$2 | Helped |
| Use LMLFM's random-effect variable selection rather than LASSO | High-dimensional longitudinal data · Simulated data, p=5000 | False positives: 415.8 -> 2.2 | Helped |
| Use LMLFM instead of M-LMM | Longitudinal depression prediction · SWAN, 15 selected variables | R² (%): 29$\pm$2 -> 30$\pm$4 | Helped |
| Use LMLFM instead of M-LMM | Longitudinal happiness prediction · GSS, 15 selected variables | R² (%): 16$\pm$1 -> 17$\pm$2 | Helped |
| Use a two-level model when the data's correlation type is misspecified | Longitudinal prediction · Simulated data with LC or CC | R² | Hurt |
| Use LMLFM to handle both fixed and random effects in high-dimensional data | Longitudinal mixed-effects prediction · Simulated data, p=5000 | Execution completion | Helped |
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