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Improve CI coverage for non-parametric CLES and rank differences #500

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mattansb opened this issue Sep 24, 2022 · 3 comments
Open

Improve CI coverage for non-parametric CLES and rank differences #500

mattansb opened this issue Sep 24, 2022 · 3 comments
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Discussion 🦜 Talking about our ~feelings~ stats enhancement 🔥 New feature or request

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@mattansb
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Open a new issue / pr to discuss improving the CI covered for all of these methods - since they are all monotonic transformations of one another, it makes sense to me they can be improved together.

Originally posted by @mattansb in #496 (comment)

Also related to #479

@mattansb mattansb changed the title Improve coverage for non-parametric CLES and rank differences Improve CI coverage for non-parametric CLES and rank differences Sep 24, 2022
@mattansb
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@arcaldwell49 feel free to contribute here if you'd like, there is not rush or pressure (:

@mattansb mattansb added enhancement 🔥 New feature or request Discussion 🦜 Talking about our ~feelings~ stats labels Sep 24, 2022
@bwiernik
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bwiernik commented Sep 29, 2022

The fisher z transformation applies to Pearson r and approximately to phi — it will undercover for any rank correlation or continuity correction like biserial, tetrachoric, or polychoric unless the n is adjusted downward

metafor::escalc has sampling error formulas for some of these I believe

@arcaldwell49
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Very good points @bwiernik . Also, I noticed an error in my code where I ran some simulations earlier this month.... The issue may be mute, but let me re-run the simulations to check.

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