easystats / easystats/performance
Outlier detection: Support Cook's distance for SEM models
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Description
In the outliers paper, we currently have the following sentence:
When no method is readily available to detect model-based outliers, such as for structural equation modeling (SEM), looking for multivariate outliers may be of relevance.
Mattan commented:
just found the
influence.SEM::genCookDist()function the does just this. [...] I think we can add that to the [check_outliers()] function. [...] I think we need Brenton's input here (:
This issue is a reminder to add this method in the future, since we are removing the reference to SEM in the paper.
Contributor 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.
Research direction
Start by reading check_outliers() and the influence.SEM::genCookDist() documentation linked in the issue. Determine how SEM models are identified and how the method should fit the existing outlier checks. Done means SEM support is added to check_outliers() and the outliers paper no longer relies on the removed SEM reference.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- r
- Domain
- data
- Issue type
- Feature
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 38/100