An Overview of Current Software Procedures for Fitting Linear Mixed Models
Bibliographic record
Abstract
At present, there are many software procedures available enabling statisticians to fit linear mixed models (LMMs) to continuous dependent variables in clustered or longitudinal data sets. LMMs are flexible tools for analyzing relationships among variables in these types of data sets, in that a variety of covariance structures can be used depending on the subject matter under study. The explicit random effects in LMMs allow analysts to make inferences about the variability between clusters or subjects in larger hypothetical populations, and examine cluster- or subject-level variables that explain portions of this variability. These models can also be used to analyze longitudinal or clustered data sets with data that are missing at random (MAR), and can accommodate time-varying covariates in longitudinal data sets. While the software procedures currently available have many features in common, more specific analytic aspects of fitting LMMs (e.g., crossed random effects, appropriate hypothesis testing for variance components, diagnostics, incorporating sampling weights) may only be available in selected software procedures. With this article, we aim to perform a comprehensive and up-to-date comparison of the current capabilities of software procedures for fitting LMMs, and provide statisticians with a guide for selecting a software procedure appropriate for their analytic goals.
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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 | 0.027 | 0.120 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.050 | 0.036 |
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".