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Jess Graves

@jessgraves.bsky.social

310 followers 330 following 340 posts

🧮 Statistics & data science 💊 Clinical trials & R&D & Epidemiology 💻 R enthusiast 👩‍💻 Stats @ loyal.com https://jesslgraves.github.io

jesslgraves.github.io

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Jess Graves @jessgraves.bsky.social · Jul 16

Better late than never — here’s my #TidyTuesday submission from last week's data!

I built a #shiny app with #plotly to explore the @xkcd.com color survey results in both 🌈 RGB and HSV space🌈.

🖥️ App: jessgraves.shinyapps.io/xkcd-color-s...
🌐 Personal site: jesslgraves.github.io/apps/2025-07...

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Reposted by Jess Graves

Jorge 🐀 @jxmartinez.bsky.social · 20d

Hooray! See y'all next year in my hometown! 🐎 #positconf #rstats #htx

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Jess Graves @jessgraves.bsky.social · 19d

🫡 yessir! Htown assemble🫡

(Thanks for tagging me, this is how I learned posit conf is coming to Houston and I’m so excited!)

Reposted by Jess Graves

Julia M. Rohrer @dingdingpeng.the100.ci · 21d

You're very welcome @vincentab.bsky.social

P.S. Check out our preprint on an alternative to staring at coefficients: j-rohrer.github.io/marginal-psy...

Manipulated theatrical release poster of "The men who stare at goats" which now reads "the men who stare at coefficients"

Top shows the profiles of George Clooney, Jeff Bridges, Ewan McGregor, Kevin Spacey and a goat.

Below the text: the men who stare at coefficients with the silhouette of a man sitting in front of a computer screen, and the silhouette of a goat

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Reposted by Jess Graves

Vincent Arel-Bundock @vincentab.bsky.social · 21d

Whoa—my book is up for pre-order!

𝐌𝐨𝐝𝐞𝐥 𝐭𝐨 𝐌𝐞𝐚𝐧𝐢𝐧𝐠: 𝐇𝐨𝐰 𝐭𝐨 𝐈𝐧𝐭𝐞𝐫𝐩𝐫𝐞𝐭 𝐒𝐭𝐚𝐭 & 𝐌𝐋 𝐌𝐨𝐝𝐞𝐥𝐬 𝐢𝐧 #Rstats 𝐚𝐧𝐝 #PyData

The book presents an ultra-simple and powerful workflow to make sense of ± any model you fit

The web version will stay free forever and my proceeds go to charity.

tinyurl.com/4fk56fc8

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Reposted by Jess Graves

Danielle Navarro @djnavarro.net · Sep 8

At the risk of stretching the imagination beyond the limits of human endurance, the reader is asked to pretend that the author is interested in splines #rstats

Splines, B-splines, P-splines, and a disapproving kitten – Notes from a data witch

No, I do not care about splines. But I am trying to learn about GAMLSS regression, and yes, it is to this dark place that this topic has taken me

blog.djnavarro.net

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Jess Graves @jessgraves.bsky.social · Sep 8

Damn! We are truly blessed to receive such quality, detail and rigor from you — and on a subject you’re not even that interested in!!🔥🔥

Reposted by Jess Graves

datavizpyr.com @datavizpyr.bsky.social · Sep 2

Selectively Remove or Hide Legends in ggplot2 datavizpyr.com/selectively-... #dataviz #rstats

Remove or Hide Legends in ggplot2 – Theme, Guides, Scales & Tips - Data Viz with Python and R

Learn how to selectively remove one or more specific legends in a plot made with ggplot2 using guides() function

datavizpyr.com

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Jess Graves @jessgraves.bsky.social · Sep 3

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Jess Graves @jessgraves.bsky.social · Aug 29

🐸🐸🐸

Cole 🐛 @colebaril.ca · Aug 29

Skipped ahead to next week's (2025-09-02) #tidytuesday because I love frogs.

#r4ds
#datavisualization

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Reposted by Jess Graves

Julia M. Rohrer @dingdingpeng.the100.ci · Aug 25

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.

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Jess Graves @jessgraves.bsky.social · Aug 20

📌

Jess Graves @jessgraves.bsky.social · Aug 20

Oh boy was she still hungry lol!

Jess Graves @jessgraves.bsky.social · Aug 20

Lil’ Muncher (official name lol) update:
1) LM abandoned his post at 50% consumption but
2) Found a friend, LM2
3) And many more 😵‍💫

Consumption rates have become exponential and sadly all (visible) Munch Bunchers had to be evicted.

$LM_{\inf} 🙃😵‍💫$

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Jess Graves @jessgraves.bsky.social · Aug 19

@mackaszechno.bsky.social @econmaett.github.io LMAO

I can debug my code, but not my garden!

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Jess Graves @jessgraves.bsky.social · Aug 19

I do intend to let it keep on keepin’ on at least for a little while haha. So a name does seem appropriate! I’ll update according lol.

Jess Graves @jessgraves.bsky.social · Aug 19

Physically, I am at my desk. Mentally, I am here:

a child is laying on the ground in a wooden box

ALT: a child is laying on the ground in a wooden box

media.tenor.com

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Jess Graves @jessgraves.bsky.social · Aug 19

*leaf 🙃🫠

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Jess Graves @jessgraves.bsky.social · Aug 19

Little muncher < 24 hrs later:

1) still on the same lead (! I was surprised by this!)
2) leaf consumption at 50%

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Jess Graves @jessgraves.bsky.social · Aug 19

Ugh I wish I had chickenssssss 😭

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Jess Graves @jessgraves.bsky.social · Aug 19

I salute thee, punster!

a woman is standing in front of an american flag with her arms outstretched and making a funny face .

ALT: a woman is standing in front of an american flag with her arms outstretched and making a funny face .

media.tenor.com

Jess Graves @jessgraves.bsky.social · Aug 18

Soon to become this absolute beast

Jess Graves @jessgraves.bsky.social · Aug 18

I’m supposed to hate this little cutie, because it will eat up my tomato plant but…. Come onnnnn look at it 🥹🥹🥹🥹

Carolina Sphinx caterpillar hanging upside down on a tomato leaf

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Reposted by Jess Graves

Gavin Simpson @gsimpson.bsky.social · Aug 18

🚀 gratia 0.11.0 is out!

Now has a paper in JOSS — please cite 📄 doi.org/10.21105/jos...

Experimental parallel processing ⚡

New assemble() for building plots 🎨

Better support for complex families + new diagnostics 🧪

Lots of bug fixes + polish ✨

👉 gavinsimpson.github.io/gratia/

#Rstats

An R package for working with generalized additive models

Graceful 'ggplot'-based graphics and utility functions for working with generalized additive models (GAMs) fitted using the 'mgcv' package.

gavinsimpson.github.io

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Jess Graves @jessgraves.bsky.social · Aug 15

📌

Jess Graves @jessgraves.bsky.social · Aug 14

*stack lol