ggeffects: Create Tidy Data Frames of Marginal Effects for ‚ggplot‘ from Model Outputs #rstats

Aim of the ggeffects-package The aim of the ggeffects-package is similar to the broom-package: transforming “untidy” input into a tidy data frame, especially for further use with ggplot. However, ggeffects does not return model-summaries; rather, this package computes marginal effects at the mean or average marginal effects from statistical models and returns the result as tidy data […]

Weiterlesen ggeffects: Create Tidy Data Frames of Marginal Effects for ‚ggplot‘ from Model Outputs #rstats

Negative Binomial Regression for Complex Samples (Surveys) #rstats

The survey-package from Thomas Lumley is a great toolkit when analyzing complex samples. It provides svyglm(), to fit generalised linear models to data from a complex survey design. svyglm() covers all families that are also provided by R’s glm() – however, the survey-package has no function to fit negative binomial models, which might be useful […]

Weiterlesen Negative Binomial Regression for Complex Samples (Surveys) #rstats

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