Jess Graves

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

🫡 yessir! Htown assemble🫡

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

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...

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

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

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

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!!🔥🔥

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

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

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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.

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...

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Oh boy was she still hungry lol!

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.

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

I can debug my code, but not my garden!

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.

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:

*leaf 🙃🫠

Little muncher < 24 hrs later:

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

Ugh I wish I had chickenssssss 😭

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!

Soon to become this absolute beast

Carolina Sphinx caterpillar hanging upside down on a tomato leaf

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

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.

🚀 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

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*stack lol

Is there any measure of Effect Size for differences assessed with Dunnett method? I know I could use the effect size pairwise, but I have a longitudinal data, where subjects were measures multiple times and there exists ICC &gt; 0.5. I guess the pooled SD may be altered by this ...

There’s a slack thread / comment on this that might be helpful? Not sure of your use case, but they warn that the pooled SD is meant to capture global variability in Y & using pairwise SDs for pairwise comparisons could mean effects aren’t broadly “standardized”

stats.stackexchange.com/a/477298

Yes!

Reminder that all three books I've co-authored are freely available online for non-commercial use (and the fourth will be, too)

Ooooh Cure is one of my favorites.

Photo of me holding a printed copy of my book

Draft cover art

Two -page spread showing graphs text and code

Two -page spread showing graphs text and code

#rstats So chuffed! I printed the first PDF copy of my book to see what it might look like in print! It will be printed in full color!

Visualizing Multivariate Data and Models in R

On the whole, looks good, but lots of tweaking to do.
It weighs in at ~440 pgs., so perhaps some cutting needed.