Blog Archives

R and labelled data: Using quasiquotation to add variable and value labels #rstats

March 19, 2019
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Labelling data is typically a task for end-users and is applied in own scripts or functions rather than in packages. However, sometimes it can be useful for both end-users and package developers to have a flexible way to add variable and value labels to their data. In such cases, quasiquotation is helpful. This vignette demonstrate how to … Weiterlesen R and...

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ggeffects 0.8.0 now on CRAN: marginal effects for regression models #rstats

January 14, 2019
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ggeffects 0.8.0 now on CRAN: marginal effects for regression models #rstats

I’m happy to announce that version 0.8.0 of my ggeffects-package is on CRAN now. The update has fixed some bugs from the previous version and comes along with many new features or improvements. One major part that was addressed in the latest version are fixed and improvements for mixed models, especially zero-inflated mixed models (fitted … Weiterlesen ggeffects 0.8.0...

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Marginal Effects for (mixed effects) regression models #rstats

November 28, 2018
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Marginal Effects for (mixed effects) regression models #rstats

ggeffects (CRAN, website) is a package that computes marginal effects at the mean (MEMs) or representative values (MERs) for many different models, including mixed effects or Bayesian models. One of the advantages of the package is its easy-to-use interface: No matter if you fit a simple or complex model, with interactions or splines, the function … Weiterlesen Marginal Effects...

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Marginal Effects for Regression Models in R #rstats #dataviz

July 3, 2018
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Marginal Effects for Regression Models in R #rstats #dataviz

Regression coefficients are typically presented as tables that are easy to understand. Sometimes, estimates are difficult to interpret. This is especially true for interaction or transformed terms (quadratic or cubic terms, polynomials, splines), in particular for more complex models. In such cases, coefficients are no longer interpretable in a direct way and marginal effects are … Weiterlesen Marginal Effects...

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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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