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ofaruksen.bsky.social
@ofaruksen.bsky.social
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vincentab.bsky.social
Hive mind, please help me out!

I need a more informative and explicit subtitle for my upcoming #RStats book "Model to Meaning"

The premise is that analysts should often transform coefficient estimates into more meaningful / interpretable quantities like predictions, risk differences, slopes, etc.
April 24, 2025 at 12:14 AM
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carlislerainey.bsky.social
I just updated my post on equivalence testing with {marginaleffects} so that it's consistent with the latest version. (Some of the notes and code were outdated.)

www.carlislerainey.com/blog/2023-08...
Equivalence Tests with {marginaleffects}
Reproducing the Clark and Golder (2006) example from Rainey (2014)
www.carlislerainey.com
June 1, 2025 at 11:16 AM
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dingdingpeng.the100.ci
At this point, I might as well --
Here's an infographic showing different ways to include age as a predictor. The top shows two extremes, just as a plain old numerical predictor (imposes linear trajectory) vs. categorical predictor (imposes nothing whatsoever). And then three solutions in between!
Infographic illustrating different ways to model age. First panel shows two "extreme" cases; including age as a linear numerical predictor (df = 1) or including age as a categorical predictor (df = number of years of age minus 1). Second panel shows an intermediate solution in which age is categorized into broader bins (df = number of categories minus 1, here 5 - 1 = 4). Third panel shows an intermediate solution in which age is included with a polynomial (df = degrees of freedom of the polynomial, here 4). Fourth panel shows an intermediate solution in which age is modeled with the help of splines (df = degrees of freedom of the splines, here 4).
July 16, 2025 at 12:33 PM
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solomonkurz.bsky.social
New #rstats blog up!

solomonkurz.netlify.app/blog/2025-07...

This is the first in a new series discussing causal inference with experimental data using multilevel models. My basic case is g-computation is the way to go.
Within-person factorial experiments, log(normal) reaction-time data | A. Solomon Kurz
Causal inference with the GLMM, Part 1
solomonkurz.netlify.app
July 21, 2025 at 2:14 PM
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vincentab.bsky.social
📚😅🎉

Yay!! I just submitted the complete manuscript of my upcoming book to the publisher!

Learn to easily and clearly interpret (almost) any stats model w/ R or Python. Simple ideas, consistent workflow, powerful tools, detailed case studies.

Read it for free @ marginaleffects.com

#RStats #PyData
Model to Meaning: How to interpret statistical models with marginaleffects for R and Python
April 10, 2025 at 7:06 PM
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dingdingpeng.the100.ci
Ever stared at a table of regression coefficients & wondered what you're doing with your life?

Very excited to share this gentle introduction to another way of making sense of statistical models (w @vincentab.bsky.social)
Preprint: doi.org/10.31234/osf...
Website: j-rohrer.github.io/marginal-psy...
Models as Prediction Machines: How to Convert Confusing Coefficients into Clear Quantities Abstract Psychological researchers usually make sense of regression models by interpreting coefficient estimates directly. This works well enough for simple linear models, but is more challenging for more complex models with, for example, categorical variables, interactions, non-linearities, and hierarchical structures. Here, we introduce an alternative approach to making sense of statistical models. The central idea is to abstract away from the mechanics of estimation, and to treat models as “counterfactual prediction machines,” which are subsequently queried to estimate quantities and conduct tests that matter substantively. This workflow is model-agnostic; it can be applied in a consistent fashion to draw causal or descriptive inference from a wide range of models. We illustrate how to implement this workflow with the marginaleffects package, which supports over 100 different classes of models in R and Python, and present two worked examples. These examples show how the workflow can be applied across designs (e.g., observational study, randomized experiment) to answer different research questions (e.g., associations, causal effects, effect heterogeneity) while facing various challenges (e.g., controlling for confounders in a flexible manner, modelling ordinal outcomes, and interpreting non-linear models). Figure illustrating model predictions. On the X-axis the predictor, annual gross income in Euro. On the Y-axis the outcome, predicted life satisfaction. A solid line marks the curve of predictions on which individual data points are marked as model-implied outcomes at incomes of interest. Comparing two such predictions gives us a comparison. We can also fit a tangent to the line of predictions, which illustrates the slope at any given point of the curve. A figure illustrating various ways to include age as a predictor in a model. On the x-axis age (predictor), on the y-axis the outcome (model-implied importance of friends, including confidence intervals). Illustrated are 1. age as a categorical predictor, resultings in the predictions bouncing around a lot with wide confidence intervals 2. age as a linear predictor, which forces a straight line through the data points that has a very tight confidence band and 3. age splines, which lies somewhere in between as it smoothly follows the data but has more uncertainty than the straight line.
August 25, 2025 at 11:49 AM