Many thanks to my amazing supervisors and co-authors @leonievogelsmeier.bsky.social, Joris Mulder, and @joranjongerling.bsky.social

Thank you very much and thank you for all your invaluable input, guidance, and support throughout this project.

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Congrats!

Then I am looking forward to reading more about your analysis in the future. Do you think using a Bayesian model with priors on the number and location of the discontinuity points could be interesting for your data?

Did you by chance publish some more details on your data and analysis somewhere? I think it would be really interesting to see more on the robustness of this figure.

Thanks for the feedback, I finally got around to increase the thickness of the fitted curves. It does look better that way.

On February 13 we will have a workshop on Modeling Non-Linear Relationships: An Introduction to Gaussian Process Regression in R and Stan by @janfailenschmid.bsky.social
More info: bit.ly/4iQSzI7
Please share!
#RStats #EconSky #AcademicSky

I am very grateful to my incredible supervisors and co-authors, @leonievogelsmeier.bsky.social , Joris Mulder, and
@joranjongerling.bsky.social, for their invaluable contributions, guidance, and endless support throughout this project.

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In this project, we review three non-parametric and non-linear regression techniques - local polynomial regression, Gaussian processes, and generalized additive models - within the context of intensive longitudinal data and compare how well these methods can recover psychological processes.

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