Simulation study for models with people observed without their partner
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Assessment
- Difficulty
- 5/5
- Estimated time
- Over a week
- Newbie friendliness
- 12/100
- Issue type
- Feature
- Clarity
- Mostly clear
- Activity status
- Active
- Tech stack
- r
- Domain
- testing-qa
Research direction
Start with the existing recovery study in dev/diagnostic_checks/simulation-studies/mixed-apim-recovery/ and the recover_exchangeable_covariance() helper it relies on, then read #88 for the singleton and missing-partner model specifications. Done means new scenarios for each listed design, with bias, RMSE, 95% Wald coverage and convergence/fit-check failures reported. This is a multi-week simulation design, so it is not a good first contribution.
Written by the indexing model from the issue text.
Description
The recovery study in dev/diagnostic_checks/simulation-studies/mixed-apim-recovery/ covers complete Gaussian dyads with 1 or 14 occasions, without missing data. It does not include people observed without their partner (#88), so we have no evidence yet on how well these models recover their parameters, or whether their confidence intervals have the intended coverage.
Designs to cover
- Singletons with complete dyads (cross-sectional): separate versus pooled residual variances (distinguishable and exchangeable), two-part partner predictors, and a separate actor slope for singletons.
- Partners not in the data: listwise deletion in models with partner predictors, compared with actor-only models.
- Status changes over time: deaths, separations and relationships forming, with the status split into within- and between-person parts, the partner's usual level split by status (
_when_exists,_before_exists,_after_exists), and lagged predictors. - Two waves: simple random-intercept models. Also check when random-slope covariance models are not identified. With exactly two centered waves, the full exchangeable actor/partner random-slope model can be unidentified.
Outcomes
- Bias and RMSE of fixed effects and of variances and covariances, including those recovered with summed indicators in
recover_exchangeable_covariance(). - Coverage of 95% Wald intervals.
- Convergence and fit-check failures.
Mixed compositions and larger sample sizes can be added if time allows. A report could join vignettes/articles/ like the existing simulation articles.
- Dominant language
- R
- Stars
- 1
- Forks
- 2
- Avg merge
- 9h 28m
- Merged PRs (30d)
- 21
Getting set up
- No Dockerfile or Docker Compose file
- No pull request template
- Read the contributing guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
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