Checking model assumption - linear models

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

Posterior predictive checks — check_predictions Posterior predictive checks mean "simulating replicated data under the fitted model and then comparing these to the observed data" (Gelman and Hill, 2007, p. 158). Posterior predictive checks can be u...

#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

Plotting estimated marginal means with tinyplot

🎉 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...

Case Study: Understanding your models

#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📦

Formatting, printing and exporting tables

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!

Estimation of Model-Based Predictions, Contrasts and Means Implements a general interface for model-based estimations for a wide variety of models, used in the computation of marginal means, contrast analysis and predictions. For a list of supported models, s...

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

Case Study: Causal inference for observational data using modelbased

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

Case Study: Measuring and comparing absolute and relative inequalities in R

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

modelbased: An R package to make the most out of your statistical models through marginal means, marginal effects, and model predictions Makowski et al., (2025). modelbased: An R package to make the most out of your statistical models through marginal means, marginal effects, and model predictions. Journal of Open Source Software, 10(1...

#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...

Framework for Easy Statistical Modeling, Visualization, and Reporting A meta-package that installs and loads a set of packages from easystats ecosystem in a single step. This collection of packages provide a unifying and consistent framework for statistical modeling, vi...

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...)

Time for a new wallpaper... #easystats #insight

An Introduction to Growth Mixture Models with brms and easystats

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/...