ggeffects/ 0000755 0001762 0000144 00000000000 14100527662 012212 5 ustar ligges users ggeffects/NAMESPACE 0000644 0001762 0000144 00000001271 14100515106 013420 0 ustar ligges users # Generated by roxygen2: do not edit by hand
S3method(plot,ggalleffects)
S3method(plot,ggeffects)
S3method(print,ggeffects)
S3method(residualize_over_grid,data.frame)
S3method(residualize_over_grid,ggeffects)
S3method(vcov,ggeffects)
export(collapse_by_group)
export(data_grid)
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(pool_predictions)
export(pretty_range)
export(representative_values)
export(residualize_over_grid)
export(show_pals)
export(theme_ggeffects)
export(values_at)
ggeffects/README.md 0000644 0001762 0000144 00000026016 14046746430 013503 0 ustar ligges users
# ggeffects - Estimated Marginal Means and Adjusted Predictions from Regression Models
[](https://cran.r-project.org/package=ggeffects)
[](https://doi.org/10.21105/joss.00772)
[](https://strengejacke.github.io/ggeffects/)
[](https://cranlogs.r-pkg.org/)
[](https://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 do we need (marginal/conditional) effects or (adjusted) predicted values?
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* or *adjusted
predictions* are far easier to understand. In particular, the
visualization of such effects or predictions 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 and adjusted predictions (or: *estimated marginal
means*) at the mean or at representative values of covariates ([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/adjusted predictions 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
modeling 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.
## Definition of “marginal effects”
There is no common language across fields regarding a unique meaning of
“marginal effects.” Thus, the wording throughout this package may vary.
Maybe “adjusted predictions” comes closest to what **ggeffects**
actually does. To avoid confusion about what is actually calculated and
returned by the package’s functions `ggpredict()`, `ggemmeans()` and
`ggeffect()`, it is recommended to read [this
vignette](https://strengejacke.github.io/ggeffects/articles/introduction_marginal_effects.html)
about the different terminology and its meanings.
## Documentation and Support
Please visit