Regularized cross-sectional network modeling with missing data: A comparison of methods
Bibliographic record
Abstract
Many applications of network modeling involve cross-sectional data of psychologicalvariables (e.g., symptoms for psychological disorders), and analyses are often conductedusing a regularized Gaussian graphical model (GGM) employing a lasso, alsoknown as the graphical lasso or glasso. Appropriate methodology for handling missingdata is underdeveloped while using glasso, precluding the use of planned missingdata designs to reduce participant fatigue. In this research, we compare three approachesto handling missing data with glasso. The first resembles a two-stage estimationapproach—–borrowed from the covariance structure modeling literature—–wherebya saturated covariance matrix among the items is estimated prior to using glasso. Thesecond and third approaches use glasso and the expectation-maximization (EM) algorithmin a single stage and either use EBIC or cross-validation for tuning parameterselection. We compared these approaches in a simulation study with a variety of samplesizes, proportions of missing data, and network saturation. An example with datafrom the Patient Reported Outcomes Measurement Information System is also provided.The EM algorithm with cross-validation performed best, but all methods appeared to beviable strategies under larger samples and with less missing data.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.079 | 0.132 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".