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easystats
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easystats
@easystats4u
Official channel of {easystats}, a collection of #rstats 📦s with a unifying and consistent framework for statistical modeling, visualization, and reporting
worldwide Katılım Ocak 2020
274 Takip Edilen6.7K Takipçiler
easystats retweetledi
🤯📈 TAKE YOUR STATS SKILLZ TO THE NEXT LEVEL WITH THIS #R PACKAGE
🔥The @easystats4u {modelbased} package (the successor of #ggeffects) is now published in @JOSS_TheOJ
joss.theoj.org/papers/10.2110…
Check it out for a demystification of marginal means, contrasts and effects #rlang
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@viv_analytics @SophieStats10 Yes, you may look at the {parameters} pkg: easystats.github.io/parameters
Tidies output, if desired, but also prints nicely and offers a lot more features (standardizing, robust standard errors, effect sizes, ...)
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We're working on revisiting and homogenizing outputs from our #easystats packages. This includes consistent coloring of information/warnings/messages, but also: which information is useful in the output, which information should just go into the docs? WDYT?
Daniel 🕹️@strengejacke
Which of the following information below model output (last paragraph, not that one about uncertainty intervals) do you find useful/helpful and think it's worth printing? It's printed once per session. Should some/all information moved into the docs, or kept in output? #easystats
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{bayestestR} makes it now much easier to process inputs from packages {marginaleffects}, {emmeans}, or random variable types from posterior draws!
#easystats #rstats
Mattan S. Ben-Shachar@mattansb
New update to {bayestestR} expands support for a tidy workflow - working better with tidy inputs, `rvar`s, and post-modeling estimates, and generating tidy outputs! @easystats4u #rstats 🧵 easystats.github.io/bayestestR/
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@schneidysenses @dp_moriarity When you install the easystats-package from CRAN, and then run `easystats::install_latest()`, does that resolve the issue for you?
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@dp_moriarity @easystats4u I routinely get the error message that a package “is not available for this version of R” and it happened just now with “performance”! I’m on 4.4.0. What version of R are folks using?
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easystats retweetledi
NEW to {bayestestR} dev version - support for #marginaleffects!
Take it for a test drive: remotes::install_github("easystats/bayestestR")
@easystats4u #rstats
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A short update on this feature:
- Improved documentation (easystats.github.io/performance/re…)
- Streamlined text output
- Improved plots
#rstats #easystats #DAG
easystats@easystats4u
A new feature that *might* be added to our #rstats #easystats packages soon: checking models for correct adjustment by using DAGs! `check_dag()` (working title) makes it so easy to check the causal paths of your model and tells you how to address misspecifications!
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@easystats4u this seems cool, but hard in practice. the same model can have different "correct" specifications based on goals (causal inference vs association vs prediction, etc.). so, output will need *lots* of warnings.
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A new feature that *might* be added to our #rstats #easystats packages soon: checking models for correct adjustment by using DAGs! `check_dag()` (working title) makes it so easy to check the causal paths of your model and tells you how to address misspecifications!
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@jpmarocox Yes, random slopes or more than one higher level should work.
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Are you working with mixed (multilevel) models in #rstats and wondering how to calculate R2? Grab the latest updates of our #easystats {performance} and {insight} packages from CRAN and try out "r2_nakagawa()" (or simply "r2()" for mixed model):
easystats.github.io/performance/re… /1
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We improved the accuracy for many new model families and validated the results against examples from the paper that has proposed this method: royalsocietypublishing.org/doi/10.1098/rs…
"r2_nakagawa()" is probably one of the most accurate functions to return R2 for mixed models in #rstats.
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easystats retweetledi
Today Tom Geva (from @bengurionu's Statisticas and Data Analysis program) presented the work he's done over the last few months for @easystats4u's correlation package 👏
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easystats retweetledi
#statstab #129 Structural Models (EFA, CFA, SEM, ...) w/ {parameters}
Thoughts: Lots of debate about #EFA vs #CFA; very confusing. Once I figure out what to use, this #R package seems to have lots of functionality.
#rstats #factoranalysis #r #stats
easystats.github.io/parameters/art…
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And another "small but great" piece in our #easystats collection...
Mattan S. Ben-Shachar@mattansb
{effectsize} has been updated! This update includes the new repeated_measures_d() function that can compute not 1, not 2, but 6 (!) types of standardized mean differences for repeated measures. Try it out: easystats.github.io/effectsize/ref… #RStats
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You may think it's just s small addition to one of our packages, but overall, the many small pieces form a marvelous "R Framework for Easy Statistical Modeling, Visualization, and Reporting" #easystats
Dominique Makowski 🧙@Dom_Makowski
A long overdue little feature just dropped: report() now works with #BayesFactor objects. Use it to make your stats reports more reproducible and easy to read. #Rstats @easystats4u
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🚨New publication in Behavior Research Methods!🚨
We review outlier detection methods and how to achieve them in R using #easystats' {performance} 📦
📄paper: doi.org/10.3758/s13428…
📃preprint: doi.org/10.31234/osf.i…
💻performance: easystats.github.io/performance/re…
#rstats
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read the case study how to arrive at the best model fit (easystats.github.io/performance/ar…), using `check_model()` and other tools from the {performance} package. Want to use the latest features? Install the {easystats} package from CRAN, run `easystats::install_latest()` and have fun! /3
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accurate tests using simulated residuals, and improved diagnostics plots (in particular, Q-Q plots). Check out the two vignettes how to check your model based on simulated residuals (easystats.github.io/performance/ar…) and /2
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Checking model assumptions is important, and doing so requires accurate tests and visuals appropriate for the given model. Hence, we revised our #easystats #rstats packages {see} and {performance}, which now provide methods to simulate residuals for complex models, to perform /1
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