Timeline for Hierarchical circular-circular regression in R
Current License: CC BY-SA 4.0
8 events
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| Mar 18, 2021 at 8:25 | vote | accept | Kuba Krukar | ||
| Mar 18, 2021 at 5:42 | comment | added | Kees Mulder |
For the second and third comment: Yes, this is right assuming condition is categorical. 'Linearizing the circular outcome' means to transform this data to the real line in some way so that we can perform linear models. In that case, we would do both (a) and (b), indeed. The other thing to add here is that with this transformation you have to check carefully if the relationship is linear.
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| Mar 18, 2021 at 5:28 | comment | added | Kees Mulder |
For the first comment: I updated the text in the main answer after 'to get'. Yes, the formula is right. No, there is not really an intuitive interpretation of the parameters (for example, note that outcome ~ cos(x + 1) + sin(x + 1) will give different coefficients but the same model/explained variance. Indeed, it's not really interpretable if the coefficients are zero. The best interpretation is then just a graph of the regression line and data in the x/y space. The best test in then indeed model comparison for adding these predictors (always together).
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| Mar 18, 2021 at 5:21 | history | edited | Kees Mulder | CC BY-SA 4.0 |
added 48 characters in body
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| Mar 17, 2021 at 16:23 | comment | added | Kuba Krukar |
And w.r.t. your last paragraph: by "linearlize the circular outcome" do you mean: (a) fitting a regular linear regression (e.g., "gaussian" model family in brms instead of "von_mises") or (b) transforming the outcome into absolute error (range: 0-180), and fitting (also as a linear regression): abs(outcome) ~ cos(predictor1) + sin(predictor1) ?
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| Mar 17, 2021 at 16:20 | comment | added | Kuba Krukar |
re: 4. I understand this would result in the formula outcome ~ condition * sin(predictor1) + condition * cos(predictor1) - is this right?
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| Mar 17, 2021 at 16:20 | comment | added | Kuba Krukar |
Thanks for your answer. I couldn't understand a few details, could you please clarify? re: 2. is some word missing after "to get" ? I don't understand this sentence. But thanks for the suggestion. So the formula would be: outcome ~ cos(predictor1) + sin(predictor1). Is there a way to intuitively interpret the resulting coefficients? What if cos(predictor1) = 0, and sin(predictor1) ≠ 0 ? Or should I rely on model comparison against the null model in order to interpret the outcome?
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| Mar 16, 2021 at 16:30 | history | answered | Kees Mulder | CC BY-SA 4.0 |