„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 […]

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

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, […]

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

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 […]

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

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) […]

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

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 […]

Weiterlesen Going Bayes #rstats

sjPlot-update: b&w-Figures for Print Journals and Package Vignettes #rstats #dataviz

My sjPlot-package was just updated on CRAN with some – as I think – useful new features. First, I have added some vignettes to the package (based on the existing online-documentation) that cover some core features and principles of the sjPlot-package, so you have direct access to these manuals within R. The vignettes are also […]

Weiterlesen sjPlot-update: b&w-Figures for Print Journals and Package Vignettes #rstats #dataviz

Exploring the European Social Survey (ESS) – pipe-friendly workflow with sjmisc, part 2 #rstats #tidyverse

This is another post of my series about how my packages integrate into a pipe-friendly workflow. The post focusses on my sjmisc-package, which was just updated on CRAN, and highlights some of the new features. Examples are based on data from the European Social Survey, which are freely available. Please note: The statistical analyses at […]

Weiterlesen Exploring the European Social Survey (ESS) – pipe-friendly workflow with sjmisc, part 2 #rstats #tidyverse

Pipe-friendly workflow with sjPlot, sjmisc and sjstats, part 1 #rstats #tidyverse

Recent development in R packages are increasingly focussing on the philosophy of tidy data and a common package design and api. Tidy data is an important part of data exploration and analysis, as shown in the following figure: Tidying data not only includes data cleaning, but also data transformation, both being necessary to perform the […]

Weiterlesen Pipe-friendly workflow with sjPlot, sjmisc and sjstats, part 1 #rstats #tidyverse

Data visualization in social sciences – what’s new in the sjPlot-package? #rstats

My sjPlot package just reached version 2.0 and got many updates during the couple of last months. The focus was less on adding new functions; rather, I improved existing functions by adding new smaller and bigger features to make working with the package easier and more reliable. In this blog post, I will report some […]

Weiterlesen Data visualization in social sciences – what’s new in the sjPlot-package? #rstats