ggeffects/ 0000755 0001762 0000144 00000000000 13614042552 012211 5 ustar ligges users ggeffects/NAMESPACE 0000644 0001762 0000144 00000004305 13614017566 013441 0 ustar ligges users # Generated by roxygen2: do not edit by hand
S3method(plot,ggalleffects)
S3method(plot,ggeffects)
S3method(print,ggeffects)
S3method(vcov,ggeffects)
export(get_complete_df)
export(get_legend_labels)
export(get_legend_title)
export(get_title)
export(get_x_labels)
export(get_x_title)
export(get_y_title)
export(ggeffect)
export(ggemmeans)
export(ggpredict)
export(new_data)
export(pretty_range)
export(representative_values)
export(show_pals)
export(theme_ggeffects)
export(values_at)
importFrom(MASS,mvrnorm)
importFrom(graphics,plot)
importFrom(insight,clean_names)
importFrom(insight,find_formula)
importFrom(insight,find_parameters)
importFrom(insight,find_predictors)
importFrom(insight,find_random)
importFrom(insight,find_response)
importFrom(insight,find_terms)
importFrom(insight,find_weights)
importFrom(insight,format_ci)
importFrom(insight,format_table)
importFrom(insight,get_data)
importFrom(insight,get_response)
importFrom(insight,get_varcov)
importFrom(insight,get_variance_random)
importFrom(insight,get_weights)
importFrom(insight,is_multivariate)
importFrom(insight,link_function)
importFrom(insight,link_inverse)
importFrom(insight,model_info)
importFrom(insight,n_obs)
importFrom(insight,print_color)
importFrom(sjlabelled,as_label)
importFrom(sjlabelled,as_numeric)
importFrom(sjlabelled,get_label)
importFrom(sjlabelled,get_labels)
importFrom(sjlabelled,set_labels)
importFrom(stats,Gamma)
importFrom(stats,as.formula)
importFrom(stats,binomial)
importFrom(stats,coef)
importFrom(stats,complete.cases)
importFrom(stats,confint)
importFrom(stats,deviance)
importFrom(stats,formula)
importFrom(stats,gaussian)
importFrom(stats,inverse.gaussian)
importFrom(stats,median)
importFrom(stats,model.matrix)
importFrom(stats,na.omit)
importFrom(stats,plogis)
importFrom(stats,poisson)
importFrom(stats,predict)
importFrom(stats,predict.glm)
importFrom(stats,qlogis)
importFrom(stats,qnorm)
importFrom(stats,quantile)
importFrom(stats,quasi)
importFrom(stats,quasibinomial)
importFrom(stats,quasipoisson)
importFrom(stats,reshape)
importFrom(stats,sd)
importFrom(stats,setNames)
importFrom(stats,simulate)
importFrom(stats,terms)
importFrom(stats,vcov)
ggeffects/README.md 0000644 0001762 0000144 00000025326 13604103340 013470 0 ustar ligges users
# ggeffects - Create Tidy Data Frames of Marginal Effects for ‘ggplot’ from Model Outputs
[](https://cran.r-project.org/package=ggeffects)
[](https://doi.org/10.21105/joss.00772)
[](https://strengejacke.github.io/ggeffects/)
[](https://travis-ci.org/strengejacke/ggeffects.svg)
[](http://cranlogs.r-pkg.org/)
[](http://cranlogs.r-pkg.org/)
Lüdecke D (2018). *ggeffects: Tidy Data Frames of Marginal Effects from
Regression Models.* Journal of Open Source Software, 3(26), 772. doi:
[10.21105/joss.00772](https://doi.org/10.21105/joss.00772)
## Why marginal effects?
Results of regression models are typically presented as tables that are
easy to understand. For more complex models that include interaction or
quadratic / spline terms, tables with numbers are less helpful and
difficult to interpret. In such cases, *marginal effects* are far easier
to understand. In particular, the visualization of marginal effects
allows to intuitively get the idea of how predictors and outcome are
associated, even for complex models.
## Aim of this package
**ggeffects** is a light-weight package that aims at easily calculating
marginal effects (or: *estimated marginal means*) at the mean or at
representative values ([see definitions
here](https://stats.stackexchange.com/tags/marginal-effect/info)) from
statistical models, i.e. predictions generated by a model when one holds
the non-focal variables constant and varies the focal variable(s). This
is achieved by three core ideas that describe the philosophy of the
function design:
1) Functions are type-safe and always return a data frame with the
same, consistent structure;
2) there is a simple, unique approach to calculate marginal effects and
estimated marginal means for many different models;
3) the package supports “labelled data” (Lüdecke 2018), which allows
human readable annotations for graphical outputs.
This means, users do not need to care about any expensive steps after
modelling to visualize the results. The returned as data frame is ready
to use with the **ggplot2**-package, however, there is also a
`plot()`-method to easily create publication-ready figures.
## Documentation and Support
Please visit