No; I did not fit multiple models. Lasso regression was used to fit a propensity model using the predictors.
Using bachelor’s vs. non-bachelor’s has advantages in interpretability, so I think this was the right move for my purposes.
I did not spend an exorbitant amount of time investigating diagnostics, for the same reason I used a proprietary package was has been built for running these tests at a production level and has been thoroughly code reviewed. I don’t think it’s worth the time to construct an overly customized analysis.
Ah, gotcha. But re: code review, even the most beautifully constructed chains can fail, and how you specify your model can easily cause things to go kabloom even if the machine’s doing everything exactly how it’s supposed to. And it only takes a few minutes to drag your log files into something like Tracer and do some basic peace-of-mind checks (and others, e.g. examine bivariate posterior distributions to assess nonidentifiably wrt your demographic params). More sophisticated diagnostics are scattered across a few programs but don’t take too long to run either (unless you have e.g. hundreds or thousands of chains, like in marginal likelihood estimation w/ stepping stones… a friend’s actually coming out with a program soon—BONSAI—that automates a lot of that grunt work, which might be worth looking out for!). :]
(on phone at gym with shit wifi so can’t provide links/refs atm, sorry!)
Sure! Though unfortunately most of the stuff comes from scattered lectures, workshops, discussions, book chapters, seminars, papers, etc. But for intro multilevel Bayesian regression in R/STAN I’d say John Kruschke’s “Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan” and Richard McElreath’s “Statistical Rethinking: A Bayesian Course with Examples in R and Stan” would be really solid (Richard also has his course lectures up on youtube if you prefer that, though I found his book super readable, so much so that when I took the class with him a few years back I skipped most of his lectures since the room was really hot. But don’t let that dissuade you from watching them, he’s a great guy/speaker and quite fun and funny!).
Purely in terms of building my own intuitions/understanding, though, I’ve found little more helpful than just looking up the relevant algorithms and implementing the damn things from scratch (to talk of reinventing square wheels above lol… though ofc you’d use the far superior underlying code others have written for your actual analysis).
No; I did not fit multiple models. Lasso regression was used to fit a propensity model using the predictors.
Using bachelor’s vs. non-bachelor’s has advantages in interpretability, so I think this was the right move for my purposes.
I did not spend an exorbitant amount of time investigating diagnostics, for the same reason I used a proprietary package was has been built for running these tests at a production level and has been thoroughly code reviewed. I don’t think it’s worth the time to construct an overly customized analysis.
Ah, gotcha. But re: code review, even the most beautifully constructed chains can fail, and how you specify your model can easily cause things to go kabloom even if the machine’s doing everything exactly how it’s supposed to. And it only takes a few minutes to drag your log files into something like Tracer and do some basic peace-of-mind checks (and others, e.g. examine bivariate posterior distributions to assess nonidentifiably wrt your demographic params). More sophisticated diagnostics are scattered across a few programs but don’t take too long to run either (unless you have e.g. hundreds or thousands of chains, like in marginal likelihood estimation w/ stepping stones… a friend’s actually coming out with a program soon—BONSAI—that automates a lot of that grunt work, which might be worth looking out for!). :]
(on phone at gym with shit wifi so can’t provide links/refs atm, sorry!)
Do you have any good textbooks or educational resources to learn these kinds of techniques?
Sure! Though unfortunately most of the stuff comes from scattered lectures, workshops, discussions, book chapters, seminars, papers, etc. But for intro multilevel Bayesian regression in R/STAN I’d say John Kruschke’s “Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan” and Richard McElreath’s “Statistical Rethinking: A Bayesian Course with Examples in R and Stan” would be really solid (Richard also has his course lectures up on youtube if you prefer that, though I found his book super readable, so much so that when I took the class with him a few years back I skipped most of his lectures since the room was really hot. But don’t let that dissuade you from watching them, he’s a great guy/speaker and quite fun and funny!).
Purely in terms of building my own intuitions/understanding, though, I’ve found little more helpful than just looking up the relevant algorithms and implementing the damn things from scratch (to talk of reinventing square wheels above lol… though ofc you’d use the far superior underlying code others have written for your actual analysis).
Sounds interesting. Would love to take a look when you get a chance to provide the links.