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Jess Graves

@jessgraves

๐Ÿงฎ Statistics & data science ๐Ÿ’Š Clinical trials & R&D & Epidemiology ๐Ÿ’ป R enthusiast ๐Ÿ‘ฉโ€๐Ÿ’ป Stats @ loyal.com https://jesslgraves.github.io

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20.11.2024
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Latest posts by Jess Graves @jessgraves

Lmao at python being a dude vaping in a coffee shop

25.02.2026 20:21 ๐Ÿ‘ 1 ๐Ÿ” 0 ๐Ÿ’ฌ 0 ๐Ÿ“Œ 0
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I tried to tell y'all.

25.02.2026 10:52 ๐Ÿ‘ 72 ๐Ÿ” 17 ๐Ÿ’ฌ 5 ๐Ÿ“Œ 2

#texas fam -early voting is here!

Reach out to your network -remind them on voting

Tomorrow is my birthday and to celebrate, I will of course go vote!

@akjackson.bsky.social @libbyheeren.bsky.social @frankiethull.bsky.social @simontrose.bsky.social @jxmartinez.bsky.social @jessgraves.bsky.social

18.02.2026 00:54 ๐Ÿ‘ 7 ๐Ÿ” 1 ๐Ÿ’ฌ 3 ๐Ÿ“Œ 0
Statistician, Research Remote - US

Hi Bluesky! Itโ€™s been a while!

Weโ€™re growing the Stats team at Loyal!

Weโ€™re looking for a statistician with experience in observational and late stage interventional clinical trials in human or vet med.

If that is you or anyone you know please apply!

job-boards.greenhouse.io/loyal36/jobs...

23.01.2026 18:27 ๐Ÿ‘ 7 ๐Ÿ” 2 ๐Ÿ’ฌ 0 ๐Ÿ“Œ 0
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If youโ€™re still hunting for color tools, Iโ€™m working on a more user-friendly version of meodai.github.io/poline/ keeping you huedrated

23.11.2025 00:42 ๐Ÿ‘ 1934 ๐Ÿ” 396 ๐Ÿ’ฌ 46 ๐Ÿ“Œ 8
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Methodology

18.11.2025 09:47 ๐Ÿ‘ 6812 ๐Ÿ” 1333 ๐Ÿ’ฌ 45 ๐Ÿ“Œ 108
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Hooray! See y'all next year in my hometown! ๐ŸŽ #positconf #rstats #htx

18.09.2025 21:33 ๐Ÿ‘ 11 ๐Ÿ” 2 ๐Ÿ’ฌ 1 ๐Ÿ“Œ 0

๐Ÿซก yessir! Htown assemble๐Ÿซก

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

19.09.2025 10:25 ๐Ÿ‘ 4 ๐Ÿ” 0 ๐Ÿ’ฌ 0 ๐Ÿ“Œ 0
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

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

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...

17.09.2025 13:31 ๐Ÿ‘ 75 ๐Ÿ” 16 ๐Ÿ’ฌ 2 ๐Ÿ“Œ 3
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Whoaโ€”my book is up for pre-order!

๐Œ๐จ๐๐ž๐ฅ ๐ญ๐จ ๐Œ๐ž๐š๐ง๐ข๐ง๐ : ๐‡๐จ๐ฐ ๐ญ๐จ ๐ˆ๐ง๐ญ๐ž๐ซ๐ฉ๐ซ๐ž๐ญ ๐’๐ญ๐š๐ญ & ๐Œ๐‹ ๐Œ๐จ๐๐ž๐ฅ๐ฌ ๐ข๐ง #Rstats ๐š๐ง๐ #PyData

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

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

tinyurl.com/4fk56fc8

17.09.2025 19:49 ๐Ÿ‘ 292 ๐Ÿ” 88 ๐Ÿ’ฌ 11 ๐Ÿ“Œ 4

Damn! We are truly blessed to receive such quality, detail and rigor from you โ€” and on a subject youโ€™re not even that interested in!!๐Ÿ”ฅ๐Ÿ”ฅ

08.09.2025 13:15 ๐Ÿ‘ 3 ๐Ÿ” 0 ๐Ÿ’ฌ 0 ๐Ÿ“Œ 0
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Remove or Hide Legends in ggplot2 โ€“ 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

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

02.09.2025 15:57 ๐Ÿ‘ 4 ๐Ÿ” 3 ๐Ÿ’ฌ 1 ๐Ÿ“Œ 1
03.09.2025 00:49 ๐Ÿ‘ 22 ๐Ÿ” 3 ๐Ÿ’ฌ 0 ๐Ÿ“Œ 1

๐Ÿธ๐Ÿธ๐Ÿธ

29.08.2025 00:46 ๐Ÿ‘ 8 ๐Ÿ” 0 ๐Ÿ’ฌ 1 ๐Ÿ“Œ 0
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 โ€œcounterfactual prediction machines,โ€ 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).

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 โ€œcounterfactual prediction machines,โ€ 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.

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.

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.

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...

25.08.2025 11:49 ๐Ÿ‘ 1007 ๐Ÿ” 288 ๐Ÿ’ฌ 47 ๐Ÿ“Œ 22

๐Ÿ“Œ

20.08.2025 14:00 ๐Ÿ‘ 0 ๐Ÿ” 0 ๐Ÿ’ฌ 0 ๐Ÿ“Œ 0

Oh boy was she still hungry lol!

20.08.2025 13:57 ๐Ÿ‘ 0 ๐Ÿ” 0 ๐Ÿ’ฌ 0 ๐Ÿ“Œ 0

Lilโ€™ Muncher (official name lol) update:
1) LM abandoned his post at 50% consumption but
2) Found a friend, LM2
3) And many more ๐Ÿ˜ตโ€๐Ÿ’ซ

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

20.08.2025 13:54 ๐Ÿ‘ 1 ๐Ÿ” 0 ๐Ÿ’ฌ 0 ๐Ÿ“Œ 0

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

I can debug my code, but not my garden!

19.08.2025 15:25 ๐Ÿ‘ 1 ๐Ÿ” 1 ๐Ÿ’ฌ 1 ๐Ÿ“Œ 0

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

19.08.2025 13:56 ๐Ÿ‘ 1 ๐Ÿ” 0 ๐Ÿ’ฌ 0 ๐Ÿ“Œ 0
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a child is laying on the ground in a wooden box ALT: a child is laying on the ground in a wooden box

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

19.08.2025 13:46 ๐Ÿ‘ 1 ๐Ÿ” 0 ๐Ÿ’ฌ 0 ๐Ÿ“Œ 0

*leaf ๐Ÿ™ƒ๐Ÿซ 

19.08.2025 13:34 ๐Ÿ‘ 1 ๐Ÿ” 0 ๐Ÿ’ฌ 1 ๐Ÿ“Œ 0

Little muncher < 24 hrs later:

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

19.08.2025 12:58 ๐Ÿ‘ 8 ๐Ÿ” 2 ๐Ÿ’ฌ 2 ๐Ÿ“Œ 1

Ugh I wish I had chickenssssss ๐Ÿ˜ญ

19.08.2025 12:55 ๐Ÿ‘ 0 ๐Ÿ” 0 ๐Ÿ’ฌ 0 ๐Ÿ“Œ 0
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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 .

I salute thee, punster!

19.08.2025 02:51 ๐Ÿ‘ 2 ๐Ÿ” 0 ๐Ÿ’ฌ 0 ๐Ÿ“Œ 0
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Soon to become this absolute beast

18.08.2025 19:52 ๐Ÿ‘ 3 ๐Ÿ” 0 ๐Ÿ’ฌ 0 ๐Ÿ“Œ 0
Carolina Sphinx caterpillar hanging upside down on a tomato leaf

Carolina Sphinx caterpillar hanging upside down on a tomato leaf

Iโ€™m supposed to hate this little cutie, because it will eat up my tomato plant butโ€ฆ. Come onnnnn look at it ๐Ÿฅน๐Ÿฅน๐Ÿฅน๐Ÿฅน

18.08.2025 19:48 ๐Ÿ‘ 8 ๐Ÿ” 1 ๐Ÿ’ฌ 3 ๐Ÿ“Œ 1
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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.

๐Ÿš€ gratia 0.11.0 is out!

Now has a paper in JOSS โ€” please cite ๐Ÿ“„ doi.org/10.21105/jos...

Experimental parallel processing โšก

New assemble() for building plots ๐ŸŽจ

Better support for complex families + new diagnostics ๐Ÿงช

Lots of bug fixes + polish โœจ

๐Ÿ‘‰ gavinsimpson.github.io/gratia/

#Rstats

18.08.2025 18:32 ๐Ÿ‘ 185 ๐Ÿ” 63 ๐Ÿ’ฌ 3 ๐Ÿ“Œ 1

๐Ÿ“Œ

15.08.2025 19:32 ๐Ÿ‘ 0 ๐Ÿ” 0 ๐Ÿ’ฌ 0 ๐Ÿ“Œ 0