Lmao at python being a dude vaping in a coffee shop
Lmao at python being a dude vaping in a coffee shop
I tried to tell y'all.
#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
Hi Bluesky! Itโs been a while!
Weโre growing the Stats team at Loyal!
Weโre 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...
If youโre still hunting for color tools, Iโm working on a more user-friendly version of meodai.github.io/poline/ keeping you huedrated
Methodology
Hooray! See y'all next year in my hometown! ๐ #positconf #rstats #htx
๐ซก yessir! Htown assemble๐ซก
(Thanks for tagging me, this is how I learned posit conf is coming to Houston and Iโm 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...
Whoaโmy book is up for pre-order!
๐๐จ๐๐๐ฅ ๐ญ๐จ ๐๐๐๐ง๐ข๐ง๐ : ๐๐จ๐ฐ ๐ญ๐จ ๐๐ง๐ญ๐๐ซ๐ฉ๐ซ๐๐ญ ๐๐ญ๐๐ญ & ๐๐ ๐๐จ๐๐๐ฅ๐ฌ ๐ข๐ง #Rstats ๐๐ง๐ #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
Damn! We are truly blessed to receive such quality, detail and rigor from you โ and on a subject youโre not even that interested in!!๐ฅ๐ฅ
Selectively Remove or Hide Legends in ggplot2 datavizpyr.com/selectively-... #dataviz #rstats
๐ธ๐ธ๐ธ
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 โcounterfactual 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.
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...
๐
Oh boy was she still hungry lol!
Lilโ Muncher (official name lol) update:
1) LM abandoned his post at 50% consumption but
2) Found a friend, LM2
3) And many more ๐ตโ๐ซ
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 keepinโ on at least for a little while haha. So a name does seem appropriate! Iโll update according lol.
*leaf ๐๐ซ
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 ๐ญ
Soon to become this absolute beast
Carolina Sphinx caterpillar hanging upside down on a tomato leaf
Iโm supposed to hate this little cutie, because it will eat up my tomato plant butโฆ. Come onnnnn look at it ๐ฅน๐ฅน๐ฅน๐ฅน
๐ gratia 0.11.0 is out!
Now has a paper in JOSS โ please cite ๐ doi.org/10.21105/jos...
Experimental parallel processing โก
New assemble() for building plots ๐จ
Better support for complex families + new diagnostics ๐งช
Lots of bug fixes + polish โจ
๐ gavinsimpson.github.io/gratia/
#Rstats
๐