Blog Archives

R functions for Bayesian Model Statistics and Summaries #rstats #stan #brms

June 6, 2018
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R functions for Bayesian Model Statistics and Summaries #rstats #stan #brms

A new update of my sjstats-package just arrived at CRAN. This blog post demontrates those functions of the sjstats-package that deal especially with Bayesian models. The update contains some new and some revised functions to compute summary statistics of Bayesian models, which are now described in more detail. hdi() rope() mcse() n_eff() tidy_stan() equi_test() mediation() … Weiterlesen R functions...

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Anova-Freak and Bayesian Hipster #rstats

March 26, 2018
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Anova-Freak and Bayesian Hipster #rstats

I’m pleased to announce an update of my sjstats-package. New features are specifically implemented for the Anova and Bayesian statistic and summary functions. Here’s a short overview of what’s new… Anova statistics Beside the already implemented functions to calculate eta-squared, partial eta-squared and omega-squared, it is now also possible to calculate partial omega-squared statistics for … Weiterlesen Anova-Freak and...

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Data transformation in #tidyverse style: package sjmisc updated #rstats

February 6, 2018
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Data transformation in #tidyverse style: package sjmisc updated #rstats

I’m pleased to announce an update for the sjmisc-package, which was just released on CRAN. Here I want to point out two important changes in the package. New default option for recoding and transformation functions First, a small change in the code with major impact on the workflow, as it affects argument defaults and is … Weiterlesen Data transformation...

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Bayesian Regression Modelling in R: Choosing informative priors in rstanarm #rstats

December 8, 2017
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Bayesian Regression Modelling in R: Choosing informative priors in rstanarm #rstats

Yesterday, at the last meeting of the Hamburg R User Group in this year, I had the pleasure to give a talk about Bayesian modelling and choosing (informative) priors in the rstanarm-package. You can download the slides of my talk here. Thanks to the Stan team and Tristan for proof reading my slides prior (

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„One function to rule them all“ – visualization of regression models in #rstats w/ #sjPlot

October 23, 2017
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„One function to rule them all“ –  visualization of regression models in #rstats w/ #sjPlot

I’m pleased to announce the latest update from my sjPlot-package on CRAN. Beside some bug fixes and minor new features, the major update is a new function, plot_model(), which is both an enhancement and replacement of sjp.lm(), sjp.glm(), sjp.lmer(), sjp.glmer() and sjp.int(). The latter functions will become deprecated in the next updates and removed somewhen

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More support for Bayesian analysis in the sj!-packages #rstats #rstan #brms

October 11, 2017
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More support for Bayesian analysis in the sj!-packages #rstats #rstan #brms

Another quick preview of my R-packages, especially sjPlot, which now also support brmsfit-objects from the great brms-package. To demonstrate the new features, I load all my „core“-packages at once, using the strengejacke-package, which is only available from GitHub. This package simply loads four packages (sjlabelled, sjmisc, sjstats and sjPlot). First, I fit two sample models, … More support for...

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Quick #sjPlot status update… #rstats #rstanarm #ggplot2

September 15, 2017
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Quick #sjPlot status update… #rstats #rstanarm #ggplot2

I’m working on the next update of my sjPlot-package, which will get a generic plot_model() method, which plots any kind of regression model, with different plot types being supported (forest plots for estimates, marginal effects and predictions, including displaying interaction terms, …). The package also supports rstan resp. rstanarm models. Since these are typically presented

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Marginal effects for negative binomial mixed effects models (glmer.nb and glmmTMB) #rstats

August 27, 2017
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Marginal effects for negative binomial mixed effects models (glmer.nb and glmmTMB) #rstats

Here’s a small preview of forthcoming features in the ggeffects-package, which are already available in the GitHub-version: For marginal effects from models fitted with glmmTMB() or glmer() resp. glmer.nb(), confidence intervals are now also computed. If you want to test these features, simply install the package from GitHub: library(devtools) devtools::install_github("strengejacke/ggeffects") Here are three examples: library(glmmTMB)

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Going Bayes #rstats

August 23, 2017
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Going Bayes #rstats

Some time ago I started working with Bayesian methods, using the great rstanarm-package. Beside the fantastic package-vignettes, and books like Statistical Rethinking or Doing Bayesion Data Analysis, I also found the ressources from Tristan Mahr helpful to both better understand Bayesian analysis and rstanarm. This motivated me to implement tools for Bayesian analysis into my

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Effect Size Statistics for Anova Tables #rstats

July 25, 2017
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Effect Size Statistics for Anova Tables #rstats

My sjstats-package has been updated on CRAN. The past updates introduced new functions for various purposes, e.g. predictive accuracy of regression models or improved support for the marvelous glmmTMB-package. The current update, however, added some ANOVA tools to the package. In this post, I want to give a short overview of these new functions, which

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