Rogue Scholar Posts

Published in quantixed

In the spirit of “if it took you a while to find out how to do something, write about it”, I will detail a method to approximate the surface area of a 3D shape. Our application here was finding the surface area of a cell but it can be used on any shape. We start with a 3D point set, specified by points of interest in a single cell imaged by 3D confocal microscopy.

Published in Andrew Heiss's blog

I’ve been finishing up a project that uses ordered Beta regression (Kubinec 2022), a neat combination of Beta regression and ordered logistic regression that you can use for modeling continuous outcomes that are bounded on either side (in my project, we’re modeling a variable that can only be between 1 and 32, for instance). It’s possible to use something like zero-one-inflated Beta regression for outcomes like this, but that kind of model

Published in Andrew Heiss's blog

I recently posted a guide (mostly for future-me) about how to analyze conjoint survey data with R. I explore two different estimands that social scientists are interested in—causal average marginal component effects (AMCEs) and descriptive marginal means—and show how to find them with R, with both frequentist and Bayesian approaches. However, that post is a little wrong. It’s not wrong wrong, but it is a bit oversimplified.

Published in Syntaxus baccata

Working on translating a key to the European shield bug nymphs (Puchkov, 1961) I thought I would look for pictures of the earlier life stages (nymphs, Fig. 1) of shield bugs (Pentatomoidea) on iNaturalist and found few observations actually had the life stage annotation.

Published in rOpenSci - open tools for open science
Authors Noam Ross, Mark Padgham

rOpenSci is very excited to announce our first peer-reviewed statistical Rpackages! One of rOpenSci’s core programs is software peer-review, where we use bestpractices from software engineering and academic peer-review to improvescientific software. Through this, we aim to make scientific software morerobust, usable, and trustworthy, and build a supportive community of practitioners.

Published in Andrew Heiss's blog

Diagrams! You can download PDF, SVG, and PNG versions of the marginal effects diagrams in this guide, as well as the original Adobe Illustrator file, here: PDFs, SVGs, and PNGs Illustrator .ai file Do whatever you want with them! They’re licensed under Creative Commons Attribution-ShareAlike (BY-SA 4.0). I’m a huge fan of doing research and analysis in public.

Published in Andrew Heiss's blog

Read the previous post first! This post is a sequel to the previous one on Bayesian propensity scores and won’t make a lot of sense without reading that one first. Read that one first! In my previous post about how to create Bayesian propensity scores and how to legally use them in a second stage outcome model, I ended up using frequentist models for the outcome stage.

Published in Andrew Heiss's blog

This post combines two of my long-standing interests: causal inference and Bayesian statistics. I’ve been teaching a course on program evaluation and causal inference for a couple years now and it has become one of my favorite classes ever.