Clik here to view.
Likert-plots and grouped Likert-plots #rstats
I’m pleased to anounce an update of my sjPlot-package, a package for Data Visualization for Statistics in Social Science. Thanks to the help of Alexander, it is now possible to create grouped...
View ArticleClik here to view.
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...
View ArticleClik here to view.
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...
View ArticleClik here to view.
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...
View ArticleClik here to view.
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,...
View ArticleClik here to view.
Likert-plots and grouped Likert-plots #rstats
I’m pleased to anounce an update of my sjPlot-package, a package for Data Visualization for Statistics in Social Science. Thanks to the help of Alexander, it is now possible to create grouped...
View ArticleClik here to view.
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...
View ArticleClik here to view.
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...
View ArticleClik here to view.
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...
View ArticleClik here to view.
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,...
View ArticleClik here to view.
Likert-plots and grouped Likert-plots #rstats
I’m pleased to anounce an update of my sjPlot-package, a package for Data Visualization for Statistics in Social Science. Thanks to the help of Alexander, it is now possible to create grouped...
View ArticleClik here to view.
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...
View ArticleClik here to view.
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...
View ArticleClik here to view.
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...
View ArticleClik here to view.
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,...
View ArticleClik here to view.
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...
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Marginal effects for negative binomial mixed effects models (glmer.nb and...
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....
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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...
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My set of packages for (daily) data analysis #rstats
I started writing my first package as collection of various functions that I needed for (almost) daily work. Meanwhile, packages were growing and bit by bit I sourced out functions to put them into...
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sjPlot-update: b&w-Figures for Print Journals and Package Vignettes #rstats...
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...
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