Hypothesis tests
Nobody has claimed this yet.
Assessment
- Difficulty
- 5/5
- Estimated time
- Over a week
- Newbie friendliness
- 35/100
Research direction
Start by reading the existing summary() output and pmrm_estimates(parameter = "theta") behavior, which already expose likelihoods, parameter counts, data sizes, estimates, and standard errors. Investigate how degrees-of-freedom approximations such as Satterthwaite could support likelihood-ratio tests of non-proportionality and significance tests for individual theta components. Done means both requested test types are available with documented results.
Written by the indexing model from the issue text.
Description
As mentioned by @larslau, we will at least want likelihood ratio tests of non-proportionality, as well as significance tests for individual $\theta$ components.
summary() already returns likelihoods, parameter counts, and data sizes, and pmrm_estimates(parameter = "theta") returns estimates and standard errors of $\theta$. We may just be missing degrees of freedom approximations. Satterthwaite seems like a good place to start. Kenward-Roger may be a lot more work.
- Dominant language
- R
- Stars
- 6
- Forks
- 2
- PR merge metrics
- No merged PRs in 30d
Getting set up
This project ships no dev container, Dockerfile or contributing guide, so setting up is up to you: start from its README, and see our first-contribution guide for the general steps.
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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