msonderegger.bsky.social
Linguistics, statistics, speech, cognitive science | McGill University Department of Linguistics
11 posts
219 followers
137 following
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I sympathize with the problem, though, and have seen people in this situation just exclude all words with frequency < some_constant, which seems worse.
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So there'd be a pseudo-word called "low_frequency_word"? I think I wouldn't do this, because it loses a lot of information... but I'm not sure what harm it'd do, if any. Probably affect the random-effect variance estimate and thus maybe SEs for word-level predictors? Curious what you find.
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could you add me? Thanks for doing this!
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not sure if you're doing Bayesian or frequentist models, but I've also used the neutralization dataset for different topics in Bayesian models here (feel free to take anything)
people.linguistics.mcgill.ca/~morgan/ling...
"smallest effect size of interest" connects nicely to ROPE
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I like your incomplete neutralization dataset! There are extensive exercises with it in my book. The 'english' and 'french_cdi' (word learning) datasets there, are also simple and intuitive, I've used them in data science courses.
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Ooh, agree. There's a section on this in my book -- random intercepts for "nuisance variables" with many levels. Shamelessly mentioning in case it helps more than 2 people read it...
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You can kind of do this using webR "Line-by-line Execution":
quarto-webr.thecoatlessprofessor.com/qwebr-code-c...
not exactly what you're saying, but close?
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Deadline extension! Abstracts for CorpusPhon are now due Wednesday, March 13 AoE.
We are also excited to announce Dr. Michael McAuliffe @mmcauliffe.bsky.social
as our invited speaker.
Hope you can join us!