Latest posts tagged with #easystats on Bluesky
Your model is only as good as its assumptions. 📊 But what happens when your data breaks the rules? Let’s dive into how to check your model assumptions—and exactly how to fix those pesky violations: 🧵👇
easystats.github.io/performance/...
#rstats #easystats #performance
#statstab #496 Posterior predictive checks {performance}
Thoughts: Idk why more frequentist don't use ppc for their models. I can diagnose so many issues visually this way.
#error #posterior #ppc #modelfit #diagnostics #model #r #rstats #easystats
easystats.github.io/performance/...
Here's a quick little example showing off tidy() vs. model_parameters(): andrewheiss.quarto.pub/parameters-v...
Maybe someday I'll make a longer, more official blog post showing glance() vs. model_performance() and augment() vs. marginaleffects::predictions() 🤷♂️ #rstats #easystats
# The columns from model_parameters() have to be named specific things to work with int_t() later return( model_parameters(model_outcome) |> insight::standardize_names(style = "broom") )
The only downside is that things like {rsample} are designed to work with broom-style names for bootstrapping, so I've got to do this name standardization thing with {insight} #rstats #easystats - example here: evalsp26.classes.andrewheiss.com/example/matc...
Finally got around to removing broom::tidy(), broom::glance(), and broom::augment() from my class examples in favor of parameters::model_parameters(), performance::model_performance() and marginaleffects::predictions() because they're *so nice* for teaching! #rstats #easystats
🎉 Great news for #rstats users! If you love the native R graphics feel of #tinyplot AND you're a fan of the powerful #easystats #modelbased package, this is for you!
Thanks to @gmcd.bsky.social, we significantly enhanced the tinyplot integration.
🔗 Read more: easystats.github.io/modelbased/a...
#statstab #463 {modelbased} Understanding your models
Thoughts: A deceptively simple case study on how to understand and report your model.
#rstats #modelling #easystats #r #reporting
easystats.github.io/modelbased/a...
This is how table printing in #easystats look like - nice tables out-of-the-box thanks to #rstats packages like {gt} or {tinytable}, which is now fully supported across easystats📦
Wanna dive deeper into the table universe? Check out these links:
👉 easystats.github.io/insight/arti...
👉 vincentarelbundock.github.io/tinytable/
Happy printing, everyone! 🖨️ #rstats #easystats
Example for a colored markdown table, printed to the R console.
That "tt" option is now fully rolled out across several #easystats packages, powered by the amazing {tinytable} package. This means you can create tables in a gazillion different output formats! How cool is that? 🤯
Screenshot of the default R console table output
... and when they print, it's thanks to some behind-the-scenes magic with `insight::format_table()` and `insight::export_table()`! ✨
But there's more! Many #easystats functions also have a `display()` method. Think of it as your personal table stylist, making everything look super user-friendly! 💅
Nice thread that gives examples how many research questions can be answered by some kind of estimated marginal means, contrasts/comparisons or marginal effects.
Check out the recent release from the #rstats {modelbased} 📦 and the cool examples shown in the #easystats thread!
Even if you're not tackling these super complex questions, {modelbased} is generally just a fantastic tool for really getting your head around your statistical models. Go on, take a peek! You might just fall in love: easystats.github.io/modelbased/
#rstats #easystats #marginaleffects #inference
True to the #easystats vibe, {modelbased} keeps things simple, flexible, and easy-peasy so you can truly unleash the power of your models without pulling your hair out.
Ever wondered about cause and effect in observational data without needing a time machine?
easystats.github.io/modelbased/a...
#easystats was a complete gamechanger for me!
That's pretty cool seeing the #easystats 📦 in teaching and daily work beyond your own little cosmos #rstats
How to summarize the total effect of a categorical variable like education? A new vignette shows how to compute absolute and relative inequality with the #easystats {modelbased}📦in #rstats. Get a single, interpretable number to quantify overall group disparities!
easystats.github.io/modelbased/a...
🎉 Great news, R users! 🎉 We're thrilled to announce that {tinyplot} support is coming to the #rstats #easystats project! Get ready for even more amazing stuff to make your data analysis a breeze! 📊✨
@gmcd.bsky.social @vincentab.bsky.social @zeileis.org
Improved support for the great {tinytable}📦 from @vincentab.bsky.social coming to the easystats packages! Use the `display()` method for different output formats of your tables - HTML, markdown, or - when `format = "tt"` a `tinytable` object that renders context-dependent.
#easystats #rstats
#statstab #390 modelbased: An R package to make the most out of
your statistical models through marginal means,
marginal effects, and model predictions
Thoughts: Great package for getting predicted probabilities for your models.
#rstats #r #easystats
doi.org/10.21105/jos...
#statstab #386 {bayestestR} Evaluating Evidence and Making Decisions using Bayesian Statistics by @mattansb.msbstats.info
Thoughts: Want to start using Bayesian stats? Here is a quick but comprehensive guide in #R
#bayesian #bayes #mcmc #easystats #guide
mattansb.github.io/bayesian-evi...
Several easystats📦were updated the past weeks, make sure to install them to get the latest features!
Here's what's new:
- 📦insight, bayestestR: performance improvements for Bayesian models, better support for brms-mixture models
1/2
#easystats #rstats
easystats.github.io/easystats/
If our packages were stocks, all our users would be rich now. But even so, you gain a lot when you use #rstats #easystats packages 😎
Yay, we have reached the 30 million downloads mark (and > 10k citations of our packages)! #easystats #rstats
(nice metrics, despite not 100% accurate, but still...)
Unlock hidden patterns in longitudinal data! 🚀 Our new vignette shows how to use brms & easystats to perform Growth Mixture Models, identify unique developmental trajectories, and visualize & interpret your findings with ease. #rstats #brms #easystats
easystats.github.io/modelbased/a...
#easystats makes #rstats easy
We're happy to have an accompanying publication for another #rstats #easystats package published! Thanks to @vincentab.bsky.social and @tjmahr.com for reviewing the manuscript!
mlmRev::egsingle |> performance::check_group_variation( select = c("female", "grade", "math"), by = c("schoolid", "childid"), include_by = TRUE ) #> Check schoolid variation #> #> Variable | Variation | Design #> ------------------------------ #> childid | both | nested #> female | within | crossed #> grade | both | #> math | both | #> #> Check childid variation #> #> Variable | Variation | Design #> ----------------------------- #> schoolid | between | #> female | between | #> grade | both | #> math | both |
🆕 Introducing check_group_variation() in the {performance} #Rstats package! 🎉
This function makes it easy to checks if variables vary within or between levels of grouping variables.
Perfect for understanding and designing mixed models 🚀
easystats.github.io/performance/...
#stats #easystats
One function per week, this time we look closer at random effects variances in mixed models: `performance_reliability()` & `performance_dvour()`. Is the variability in your data due to noise within groups, or actual differences between groups? #easystats #rstats easystats.github.io/performance/...
