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Andrew Heiss :rstats:<p>New blog post! Seven (7!) new tidyexplain-esque animations showing how {dplyr}'s mutate(), summarize(), group_by(), and ungroup() all work together <a href="https://fediscience.org/tags/rstats" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>rstats</span></a> <a href="https://fediscience.org/tags/statsodon" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>statsodon</span></a> <a href="https://www.andrewheiss.com/blog/2024/04/04/group_by-summarize-ungroup-animations/" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://www.</span><span class="ellipsis">andrewheiss.com/blog/2024/04/0</span><span class="invisible">4/group_by-summarize-ungroup-animations/</span></a></p>
Andrew Heiss :rstats:<p>New blog post! Have you (like me!) wondered what the ATT means in causal inference and how it's different from average treatment effects (ATE)? I use <a href="https://fediscience.org/tags/rstats" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>rstats</span></a> to explore why we care about the ATE, ATT, and ATU and show how to calculate them with observational data! <a href="https://www.andrewheiss.com/blog/2024/03/21/demystifying-ate-att-atu/" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://www.</span><span class="ellipsis">andrewheiss.com/blog/2024/03/2</span><span class="invisible">1/demystifying-ate-att-atu/</span></a> <a href="https://fediscience.org/tags/statsodon" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>statsodon</span></a></p>
Andrew Heiss :rstats:<p>This paper by <span class="h-card" translate="no"><a href="https://sciences.social/@nickchk" class="u-url mention" rel="nofollow noopener" target="_blank">@<span>nickchk</span></a></span> (<a href="https://doi.org/10.1080/1350178X.2022.2088085" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">doi.org/10.1080/1350178X.2022.</span><span class="invisible">2088085</span></a> ; ungated here: https: //ftp.cs.ucla.edu/pub/stat_ser/huntington-klein-jem-june2022.pdf) is the best, most accessible introduction and explanation of how DAGs can be useful for causal inference for people more familiar with potential outcomes and econometrics-style approaches <a href="https://fediscience.org/tags/statsodon" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>statsodon</span></a> <a href="https://fediscience.org/tags/CausalInference" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>CausalInference</span></a></p>
Andrew Heiss :rstats:<p>It's DAG day in class today and I *think* figured out a way to animatedly demonstrate collider bias (at least the selection bias version of it) <a href="https://fediscience.org/tags/CausalInference" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>CausalInference</span></a> <a href="https://fediscience.org/tags/Statsodon" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Statsodon</span></a></p>
Andrew Heiss :rstats:<p>Update! The actual reason I had to figure out this distribution is because the ordered beta model I was using used it to define priors for the values *between* the Dirichlet columns. The post now shows how to work with those cutpoints <a href="https://fediscience.org/tags/rstats" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>rstats</span></a> <a href="https://fediscience.org/tags/statsodon" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>statsodon</span></a> <a href="https://fediscience.org/tags/Bayesian" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Bayesian</span></a> <a href="https://www.andrewheiss.com/blog/2023/09/18/understanding-dirichlet-beta-intuition/#bonus-later-addition-boundaries-between-categories" rel="nofollow noopener" target="_blank"><span class="invisible">https://www.</span><span class="ellipsis">andrewheiss.com/blog/2023/09/1</span><span class="invisible">8/understanding-dirichlet-beta-intuition/#bonus-later-addition-boundaries-between-categories</span></a></p>
Andrew Heiss :rstats:<p>New post! Have you (like me) been confused/intimidated by Dirichlet distributions? Here's a basic, visual-heavy, intuition-heavy guide to the Dirichlet distribution. It's just a Beta distribution, but ~fancy~! <a href="https://fediscience.org/tags/rstats" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>rstats</span></a> <a href="https://fediscience.org/tags/statsodon" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>statsodon</span></a> <a href="https://www.andrewheiss.com/blog/2023/09/18/understanding-dirichlet-beta-intuition/" rel="nofollow noopener" target="_blank"><span class="invisible">https://www.</span><span class="ellipsis">andrewheiss.com/blog/2023/09/1</span><span class="invisible">8/understanding-dirichlet-beta-intuition/</span></a></p>
Andrew Heiss :rstats:<p>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 <a href="https://fediscience.org/tags/rstats" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>rstats</span></a> <a href="https://fediscience.org/tags/bayesian" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>bayesian</span></a> <a href="https://fediscience.org/tags/statsodon" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>statsodon</span></a></p>
Andrew Heiss :rstats:<p>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 <a href="https://www.andrewheiss.com/blog/2022/05/20/marginalia/" rel="nofollow noopener" target="_blank"><span class="invisible">https://www.</span><span class="ellipsis">andrewheiss.com/blog/2022/05/2</span><span class="invisible">0/marginalia/</span></a> <a href="https://fediscience.org/tags/rstats" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>rstats</span></a> <a href="https://fediscience.org/tags/statsodon" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>statsodon</span></a></p>
Andrew Heiss :rstats:<p>Check out this new ultimate guide to multilevel/hierarchical multinomial conjoint analysis with <a href="https://fediscience.org/tags/rstats" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>rstats</span></a> and {brms}, including how to find both marketing-style predicted market shares *and* polisci-style causal effects *across individual covariates* <a href="https://fediscience.org/tags/bayesian" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>bayesian</span></a> <a href="https://fediscience.org/tags/statsodon" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>statsodon</span></a> </p><p><a href="https://www.andrewheiss.com/blog/2023/08/12/conjoint-multilevel-multinomial-guide/" rel="nofollow noopener" target="_blank"><span class="invisible">https://www.</span><span class="ellipsis">andrewheiss.com/blog/2023/08/1</span><span class="invisible">2/conjoint-multilevel-multinomial-guide/</span></a></p>
Andrew Heiss :rstats:<p>Here it is! The ultimate practical guide to Bayesian and frequentist conjoint data analysis with <a href="https://fediscience.org/tags/rstats" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>rstats</span></a> and {brms} and {marginaleffects}, including how to distinguish between marginal effects and marginal means + work with subgroups! <a href="https://fediscience.org/tags/statsodon" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>statsodon</span></a> <a href="https://www.andrewheiss.com/blog/2023/07/25/conjoint-bayesian-frequentist-guide/" rel="nofollow noopener" target="_blank"><span class="invisible">https://www.</span><span class="ellipsis">andrewheiss.com/blog/2023/07/2</span><span class="invisible">5/conjoint-bayesian-frequentist-guide/</span></a></p>
Philip N Cohen<p>OLS Strikes Back <br><a href="https://mastodon.social/tags/academia" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>academia</span></a> <a href="https://mastodon.social/tags/statsodon" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>statsodon</span></a> <a href="https://mastodon.social/tags/SAG" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>SAG</span></a> <br>ht <span class="h-card" translate="no"><a href="https://sciences.social/@WeedenKim" class="u-url mention" rel="nofollow noopener" target="_blank">@<span>WeedenKim</span></a></span></p>
Andrew Heiss :rstats:<p>New blog post! Here's a guide to calculating the differences between categorical proportions in a principled, <a href="https://fediscience.org/tags/bayesian" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>bayesian</span></a> way with <a href="https://fediscience.org/tags/rstats" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>rstats</span></a>, <a href="https://fediscience.org/tags/mcmcstan" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>mcmcstan</span></a>, and {brms}, including fancy things like mosaic plots (with {ggmosaic} and striped fills (with {ggpattern}) <a href="https://www.andrewheiss.com/blog/2023/05/15/fancy-bayes-diffs-props/" rel="nofollow noopener" target="_blank"><span class="invisible">https://www.</span><span class="ellipsis">andrewheiss.com/blog/2023/05/1</span><span class="invisible">5/fancy-bayes-diffs-props/</span></a> <a href="https://fediscience.org/tags/statsodon" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>statsodon</span></a></p>
Ewan Donnachie<p><span class="h-card"><a href="https://fediscience.org/@ElenLeFoll" class="u-url mention" rel="nofollow noopener" target="_blank">@<span>ElenLeFoll</span></a></span> <br>Can't think of one off the top of my head, but hopefully someone on <a href="https://mstdn.social/tags/statsodon" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>statsodon</span></a> has a favourite teaching example.<br><a href="https://mstdn.social/tags/Statistics" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Statistics</span></a></p>
Andrew Heiss :rstats:<p>The 8th iteration of my <a href="https://fediscience.org/tags/ProgramEvaluation" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>ProgramEvaluation</span></a> and <a href="https://fediscience.org/tags/CausalInference" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>CausalInference</span></a> course is up and live at <a href="https://evalsp23.classes.andrewheiss.com/" rel="nofollow noopener" target="_blank"><span class="invisible">https://</span><span class="ellipsis">evalsp23.classes.andrewheiss.c</span><span class="invisible">om/</span></a> ! </p><p>It covers basic econometrics and DAGs, all with <a href="https://fediscience.org/tags/rstats" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>rstats</span></a>, 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! <a href="https://fediscience.org/tags/epitwitter" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>epitwitter</span></a> <a href="https://fediscience.org/tags/EconTwitter" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>EconTwitter</span></a> <a href="https://fediscience.org/tags/statsodon" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>statsodon</span></a></p>
Andrew Heiss :rstats:<p>Finally using my recent blog post on conditional vs. marginal effects in multilevel models (<a href="https://www.andrewheiss.com/blog/2022/11/29/conditional-marginal-marginaleffects/" rel="nofollow noopener" target="_blank"><span class="invisible">https://www.</span><span class="ellipsis">andrewheiss.com/blog/2022/11/2</span><span class="invisible">9/conditional-marginal-marginaleffects/</span></a>) with some real data in a long-running project I'm working on and it's SO NEAT <a href="https://fediscience.org/tags/rstats" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>rstats</span></a> <a href="https://fediscience.org/tags/statsodon" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>statsodon</span></a></p>
Andrew Heiss :rstats:<p>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*. </p><p>I recreate a post by <span class="h-card"><a href="https://mastodon.rpsychologist.com/@kristoffer" class="u-url mention" rel="nofollow noopener" target="_blank">@<span>kristoffer</span></a></span> to show the differences between the two kinds of effects using <span class="h-card"><a href="https://fosstodon.org/@vincentab" class="u-url mention" rel="nofollow noopener" target="_blank">@<span>vincentab</span></a></span> 's phenomenal {marginaleffects} package</p><p><a href="https://www.andrewheiss.com/blog/2022/11/29/conditional-marginal-marginaleffects/" rel="nofollow noopener" target="_blank"><span class="invisible">https://www.</span><span class="ellipsis">andrewheiss.com/blog/2022/11/2</span><span class="invisible">9/conditional-marginal-marginaleffects/</span></a></p><p><a href="https://fediscience.org/tags/rstats" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>rstats</span></a> <a href="https://fediscience.org/tags/statsodon" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>statsodon</span></a> <a href="https://fediscience.org/tags/bayesian" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>bayesian</span></a> <a href="https://fediscience.org/tags/MultilevelModels" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>MultilevelModels</span></a></p>
Andrew Heiss :rstats:<p>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 <a href="https://fediscience.org/tags/statsodon" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>statsodon</span></a></p>
Amy Hogan<p>Hello Mathstodon! I am Amy Hogan, NYC high school teacher. Currently teaching AP Statistics, Math Analysis (sophomore math team), and Algebra 2. I am involved with stats education at the K-12 level with the ASA and NCTM, and serving on the committee for USCOTS 2023. </p><p>What else should I include in this introduction?</p><p>Edited to add hashtags: <a href="https://mathstodon.xyz/tags/APStats" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>APStats</span></a> <a href="https://mathstodon.xyz/tags/statistics" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>statistics</span></a> <a href="https://mathstodon.xyz/tags/statsodon" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>statsodon</span></a> <a href="https://mathstodon.xyz/tags/MTBOS" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>MTBOS</span></a> <a href="https://mathstodon.xyz/tags/iteachmath" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>iteachmath</span></a> <a href="https://mathstodon.xyz/tags/statsed" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>statsed</span></a> <a href="https://mathstodon.xyz/tags/USCOTS23" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>USCOTS23</span></a></p>
Brenton Wiernik 🏳️‍🌈<p>Can folks help me think through smtg:<br>I’ve got a 5-point Likert response with 1 binary predictor. If I dichotomize it into Agree/Str Agree vs other 3 groups and model as binomial, that is equivalent to a constrained multinomial model. What are those constraints? <a href="https://tech.lgbt/tags/Stats" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Stats</span></a> <a href="https://tech.lgbt/tags/Statsodon" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Statsodon</span></a> <a href="https://tech.lgbt/tags/brms" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>brms</span></a></p>
Andrew Heiss :rstats:<p>More *Bayes Rules!* notes + <a href="https://fediscience.org/tags/brms" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>brms</span></a> <a href="https://fediscience.org/tags/rstats" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>rstats</span></a> translation: logistic and negative binomial models, which are super tricky to work with!</p><p>I added extra stuff on marginal effects, including the (new!) ability to integrate out random effects with {marginaleffects}</p><p><a href="https://bayesf22-notebook.classes.andrewheiss.com/bayes-rules/18-chapter.html" rel="nofollow noopener" target="_blank"><span class="invisible">https://</span><span class="ellipsis">bayesf22-notebook.classes.andr</span><span class="invisible">ewheiss.com/bayes-rules/18-chapter.html</span></a></p><p><a href="https://fediscience.org/tags/bayesian" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>bayesian</span></a> <a href="https://fediscience.org/tags/statTootstics" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>statTootstics</span></a> <a href="https://fediscience.org/tags/statsodon" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>statsodon</span></a></p>
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