yessssss this brms bayesian model for a conjoint survey experiment took 3 hours to fit, estimated nearly 40,000 unique parameters, takes up 4 GB of space, and is most definitely way overkill, but IT CONVERGED AND WORKS GLORIOUSLY #rstats #bayesian #statsodon

Also, in the course of adding DOIs to past posts, I updated my big ol' guide to different flavors of marginal effects to use {marginaleffects}'s newer slopes(), predictions(), and comparisons() functions https://www.andrewheiss.com/blog/2022/05/20/marginalia/ #rstats #statsodon

Q about #bayesian stuff: I'm finding the probability of direction (proportion of posterior that's >0 or <0), but I never know how to report these, since sometimes they're positive and sometimes they're negative. My current solution is to use a column for each direction—is there a better way tho? #statsodon

Check out this new ultimate guide to multilevel/hierarchical multinomial conjoint analysis with #rstats and {brms}, including how to find both marketing-style predicted market shares *and* polisci-style causal effects *across individual covariates* #bayesian #statsodon

https://www.andrewheiss.com/blog/2023/08/12/conjoint-multilevel-multinomial-guide/

heck yes full-luxury multilevel multinomial models with brms #rstats #bayesian #statsodon

Here it is! The ultimate practical guide to Bayesian and frequentist conjoint data analysis with #rstats and {brms} and {marginaleffects}, including how to distinguish between marginal effects and marginal means + work with subgroups! #statsodon https://www.andrewheiss.com/blog/2023/07/25/conjoint-bayesian-frequentist-guide/

throwing some do() operators into this conjoint blog post so you know it's serious causal inference #CausalInference #statsodon

random walk, n., the route through an open plan office taken by a #statistician whilst waiting for their #MCMC chains to converge (hopefully). Expected length of route may depend on factors such as hardware specifications, informativeness of #priors, and whether there's a good coffee machine nearby. #statsodon #Bayesian #iamworking @pymc

ahhh this new #bayesian ordered beta regression model family (package: https://github.com/saudiwin/ordbetareg_pack; paper: https://doi.org/10.1017/pan.2022.20) is so so neat! I have an outcome bounded at 1 and 32, and the model successfully predicts discrete 1s and 32s, as well as a continuous range in between! #rstats #brms #statsodon

Talking about DAG colliders in my program evaluation class tonight, so back to my good ol' standby examples of niceness → appearance + race → police use of force #statsodon #CausalInference

Love this consistency! #rstats #statsodon

The 8th iteration of my #ProgramEvaluation and #CausalInference course is up and live at https://evalsp23.classes.andrewheiss.com/ !

It covers basic econometrics and DAGs, all with #rstats, and it's mostly asynchronous with dozens of hours of videos, and the whole thing is Creative Commons-licensed, so do whatever you want with it! #epitwitter #EconTwitter #statsodon

It can sometimes take a while to make regression tables for #bayesian #brms models, but there's a cool feature in {modelsummary} where you can store the heavy calculations in an output-agnostic format and then display it however #rstats (reprex: https://gist.github.com/andrewheiss/644511963e8b10c81b23c26529a93818 ) #statsodon

@mastodonusercount is a fun bot to follow.

Last day (12 hrs ago) +23,749 vs last day +51,112 (now)

#statsodon

Like, I even teach that randomized promotion of a program should be analyzed as an IV (see https://evalf22.classes.andrewheiss.com/content/12-content.html) (and they talk about that idea in the podcast too), but I never made the connection between IVs and RCTs in general haha #statsodon #statTootstics

This latest episode of the Casual Inference podcast on instrumental variables is fantastic and it’s neat to hear about IV from a non-econometrics perspective https://casualinfer.libsyn.com/website/instrumental-variables-with-maria-glymour-season-4-episode-5

and my post on conditional and marginal effects makes a surprise appearance at the beginning lol https://www.andrewheiss.com/blog/2022/11/29/conditional-marginal-marginaleffects/

#statsodon #causaltwitter

Finally using my recent blog post on conditional vs. marginal effects in multilevel models (https://www.andrewheiss.com/blog/2022/11/29/conditional-marginal-marginaleffects/) with some real data in a long-running project I'm working on and it's SO NEAT #rstats #statsodon

New post! If you think of "marginal effects" as slopes, the terms "marginal effects" and "conditional effects" aren't quite the same thing in the world of multilevel models, which is *so confusing*.

I recreate a post by @kristoffer to show the differences between the two kinds of effects using @vincentab 's phenomenal {marginaleffects} package

https://www.andrewheiss.com/blog/2022/11/29/conditional-marginal-marginaleffects/

#rstats #statsodon #bayesian #MultilevelModels

I'm at the point where I have to actually tackle this question for the dissertation (also still need to do a bunch of simulation for my BMAME approach), but am torn so pitching this to #statsodon and #bayesians in the hope that someone either has a better answer than I do or can tell me that I'm just wildly overthinking this 🤷‍♂️

working on a new blog post about "fixed effects" and how they're, like, conceptual opposites depending on the kind of model you're using or the discipline you're working with #statsodon