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If you adopt a Bayesian approach then you should be able to fit such a model. This can be estimated using Markov Chain Monte Carlo for example with JAGS (Just Another Gibbs Sampler) or Hamilton Monte Carlo with Stan. Both can be set up and run from within R. I appreciate that you say it may take a long time to run, but there are ways to speed things up such ...


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I'll answer your questions in reverse. Your model posits that the log odds of "jumping the hurdle" look like $$ \operatorname{logit}(p) = \hat{\beta}_0 + \hat{\beta}_1\operatorname{Start}$$ With $\hat{\beta}_0 = 0.51$ and $\hat{\beta}_1 \approx 0$. To obtain the binomial probability, invert the logit using $$ p = \dfrac{1}{1+\exp(-(\hat{\beta}_0 + \hat{\...


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Came across a current online review piece on 'Zero-One Inflated Beta Models', by Karen Grace-Martin in "The Analysis Factor", outlining the proposed solution (noted above by Matze O in 2013) to address the 0/1 occurrence issue. To quote parts from the non-technical review: So if a client takes their medication 30 out of 30 days, a beta regression won’t ...


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My experience with regression modeling, in general, involves also the exercise of judgment and in that regard, it is something of an art, especially when it comes to constructing good forecasting models. My observation is that actually parsimonious models appear to be superior, overfitting a model is not the apparent optimal best approach. My experience ...


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