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‎README.Rmd

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```
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[![DOI](https://joss.theoj.org/papers/10.21105/joss.03139/status.svg)](https://doi.org/10.21105/joss.03139)
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[![downloads](http://cranlogs.r-pkg.org/badges/performance)](https://cran.r-project.org/package=performance) [![total](https://cranlogs.r-pkg.org/badges/grand-total/performance)](https://cranlogs.r-pkg.org/)
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[![downloads](https://cranlogs.r-pkg.org/badges/performance)](https://cran.r-project.org/package=performance) [![total](https://cranlogs.r-pkg.org/badges/grand-total/performance)](https://cranlogs.r-pkg.org/)
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***Test if your model is a good model!***
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## Installation
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[![CRAN](http://www.r-pkg.org/badges/version/performance)](https://cran.r-project.org/package=performance) [![performance status badge](https://easystats.r-universe.dev/badges/performance)](https://easystats.r-universe.dev) [![R check](https://github.com/easystats/performance/workflows/R-CMD-check/badge.svg?branch=main)](https://github.com/easystats/performance/actions)
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[![CRAN](https://www.r-pkg.org/badges/version/performance)](https://cran.r-project.org/package=performance) [![performance status badge](https://easystats.r-universe.dev/badges/performance)](https://easystats.r-universe.dev) [![codecov](https://codecov.io/gh/easystats/performance/branch/main/graph/badge.svg)](https://app.codecov.io/gh/easystats/performance)
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The *performance* package is available on CRAN, while its latest development version is available on R-universe (from _rOpenSci_).
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> **Tip**
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>
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> Instead of `library(performance)`, use `library(easystats)`. This will make all features of the easystats-ecosystem available.
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> Instead of `library(performance)`, use `library(easystats)`. This will make all features of the easystats-ecosystem available.
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>
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> To stay updated, use `easystats::install_latest()`.
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## Documentation
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[![Documentation](https://img.shields.io/badge/documentation-performance-orange.svg?colorB=E91E63)](https://easystats.github.io/performance/)
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[![Blog](https://img.shields.io/badge/blog-easystats-orange.svg?colorB=FF9800)](https://easystats.github.io/blog/posts/)
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[![Features](https://img.shields.io/badge/features-performance-orange.svg?colorB=2196F3)](https://easystats.github.io/performance/reference/index.html)
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There is a nice introduction into the package on [youtube](https://www.youtube.com/watch?v=EPIxQ5i5oxs).
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## The *performance* workflow

‎README.md

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# performance <img src='man/figures/logo.png' align="right" height="139" />
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[![DOI](https://joss.theoj.org/papers/10.21105/joss.03139/status.svg)](https://doi.org/10.21105/joss.03139)
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[![downloads](http://cranlogs.r-pkg.org/badges/performance)](https://cran.r-project.org/package=performance)
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[![downloads](https://cranlogs.r-pkg.org/badges/performance)](https://cran.r-project.org/package=performance)
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[![total](https://cranlogs.r-pkg.org/badges/grand-total/performance)](https://cranlogs.r-pkg.org/)
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***Test if your model is a good model!***
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## Installation
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[![CRAN](http://www.r-pkg.org/badges/version/performance)](https://cran.r-project.org/package=performance)
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[![CRAN](https://www.r-pkg.org/badges/version/performance)](https://cran.r-project.org/package=performance)
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[![performance status
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badge](https://easystats.r-universe.dev/badges/performance)](https://easystats.r-universe.dev)
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[![R
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check](https://github.com/easystats/performance/workflows/R-CMD-check/badge.svg?branch=main)](https://github.com/easystats/performance/actions)
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[![codecov](https://codecov.io/gh/easystats/performance/branch/main/graph/badge.svg)](https://app.codecov.io/gh/easystats/performance)
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The *performance* package is available on CRAN, while its latest
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development version is available on R-universe (from *rOpenSci*).
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| Type | Source | Command |
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|-------------|------------|-------------------------------------------------------------------------------|
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| Release | CRAN | `install.packages("performance")` |
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| Type | Source | Command |
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|----|----|----|
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| Release | CRAN | `install.packages("performance")` |
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| Development | R-universe | `install.packages("performance", repos = "https://easystats.r-universe.dev")` |
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Once you have downloaded the package, you can then load it using:
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``` r
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citation("performance")
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#> To cite package 'performance' in publications use:
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#>
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#>
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#> Lüdecke et al., (2021). performance: An R Package for Assessment, Comparison and
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#> Testing of Statistical Models. Journal of Open Source Software, 6(60), 3139.
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#> https://doi.org/10.21105/joss.03139
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#>
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#>
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#> A BibTeX entry for LaTeX users is
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#>
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#>
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#> @Article{,
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#> title = {{performance}: An {R} Package for Assessment, Comparison and Testing of Statistical Models},
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#> author = {Daniel Lüdecke and Mattan S. Ben-Shachar and Indrajeet Patil and Philip Waggoner and Dominique Makowski},
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## Documentation
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[![Documentation](https://img.shields.io/badge/documentation-performance-orange.svg?colorB=E91E63)](https://easystats.github.io/performance/)
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[![Blog](https://img.shields.io/badge/blog-easystats-orange.svg?colorB=FF9800)](https://easystats.github.io/blog/posts/)
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[![Features](https://img.shields.io/badge/features-performance-orange.svg?colorB=2196F3)](https://easystats.github.io/performance/reference/index.html)
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There is a nice introduction into the package on
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[youtube](https://www.youtube.com/watch?v=EPIxQ5i5oxs).
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r2(model)
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#> # Bayesian R2 with Compatibility Interval
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#>
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#>
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#> Conditional R2: 0.954 (95% CI [0.951, 0.957])
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#> Marginal R2: 0.414 (95% CI [0.204, 0.644])
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library(lme4)
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model <- lmer(Reaction ~ Days + (1 + Days | Subject), data = sleepstudy)
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r2(model)
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#> # R2 for Mixed Models
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#>
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#>
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#> Conditional R2: 0.799
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#> Marginal R2: 0.279
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```
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model <- lmer(Reaction ~ Days + (1 + Days | Subject), data = sleepstudy)
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icc(model)
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#> # Intraclass Correlation Coefficient
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#>
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#>
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#> Adjusted ICC: 0.722
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#> Unadjusted ICC: 0.521
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```
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``` r
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icc(model)
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#> # Intraclass Correlation Coefficient
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#>
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#> Adjusted ICC: 0.941
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#> Unadjusted ICC: 0.779
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#>
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#> Adjusted ICC: 0.930
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#> Unadjusted ICC: 0.771
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```
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### Model diagnostics
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model <- glm(count ~ spp + mined, family = poisson, data = Salamanders)
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check_overdispersion(model)
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#> # Overdispersion test
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#>
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#>
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#> dispersion ratio = 2.946
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#> Pearson's Chi-Squared = 1873.710
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model <- glm(count ~ spp + mined, family = poisson, data = Salamanders)
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check_zeroinflation(model)
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#> # Check for zero-inflation
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#>
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#> Observed zeros: 387
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#> Predicted zeros: 298
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#> Ratio: 0.77
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m1 <- lm(mpg ~ wt + cyl, data = mtcars)
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model_performance(m1)
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#> # Indices of model performance
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#>
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#>
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#> AIC | AICc | BIC | R2 | R2 (adj.) | RMSE | Sigma
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#> ---------------------------------------------------------------
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#> 156.010 | 157.492 | 161.873 | 0.830 | 0.819 | 2.444 | 2.568
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m2 <- glm(vs ~ wt + mpg, data = mtcars, family = "binomial")
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model_performance(m2)
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#> # Indices of model performance
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#>
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#>
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#> AIC | AICc | BIC | Tjur's R2 | RMSE | Sigma | Log_loss | Score_log | Score_spherical | PCP
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#> -----------------------------------------------------------------------------------------------------
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#> 31.298 | 32.155 | 35.695 | 0.478 | 0.359 | 1.000 | 0.395 | -14.903 | 0.095 | 0.743
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model_performance(m3)
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#> # Indices of model performance
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#>
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#> AIC | AICc | BIC | R2 (cond.) | R2 (marg.) | ICC | RMSE | Sigma
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#> ----------------------------------------------------------------------------------
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#> 1755.628 | 1756.114 | 1774.786 | 0.799 | 0.279 | 0.722 | 23.438 | 25.592
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#> # Comparison of Model Performance Indices
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#>
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#> Name | Model | AIC (weights) | AICc (weights) | BIC (weights) | RMSE | Sigma | Score_log | Score_spherical | R2 | R2 (adj.) | Tjur's R2 | Log_loss | PCP | R2 (cond.) | R2 (marg.) | ICC | Nagelkerke's R2
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#> ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
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#> m1 | lm | 156.0 (<.001) | 157.5 (<.001) | 161.9 (<.001) | 2.444 | 2.568 | | | 0.830 | 0.819 | | | | | | |
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#> m2 | glm | 31.3 (>.999) | 32.2 (>.999) | 35.7 (>.999) | 0.359 | 1.000 | -14.903 | 0.095 | | | 0.478 | 0.395 | 0.743 | | | |
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#> m3 | lmerMod | 1764.0 (<.001) | 1764.5 (<.001) | 1783.1 (<.001) | 23.438 | 25.592 | | | | | | | | 0.799 | 0.279 | 0.722 |
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#> m4 | glm | 56.8 (<.001) | 76.8 (<.001) | 57.7 (<.001) | 3.043 | 1.000 | -2.598 | 0.324 | | | | | | | | | 0.657
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#>
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#> Name | Model | AIC (weights) | AICc (weights) | BIC (weights) | RMSE | Sigma | Score_log
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#> -----------------------------------------------------------------------------------------------
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#> m1 | lm | 156.0 (<.001) | 157.5 (<.001) | 161.9 (<.001) | 2.444 | 2.568 |
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#> m2 | glm | 31.3 (>.999) | 32.2 (>.999) | 35.7 (>.999) | 0.359 | 1.000 | -14.903
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#> m3 | lmerMod | 1764.0 (<.001) | 1764.5 (<.001) | 1783.1 (<.001) | 23.438 | 25.592 |
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#> m4 | glm | 56.8 (<.001) | 76.8 (<.001) | 57.7 (<.001) | 3.043 | 1.000 | -2.598
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#>
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#> Name | Score_spherical | R2 | R2 (adj.) | Tjur's R2 | Log_loss | PCP | R2 (cond.) | R2 (marg.)
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#> ---------------------------------------------------------------------------------------------------
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#> m1 | | 0.830 | 0.819 | | | | |
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#> m2 | 0.095 | | | 0.478 | 0.395 | 0.743 | |
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#> m3 | | | | | | | 0.799 | 0.279
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#> m4 | 0.324 | | | | | | |
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#>
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#> Name | ICC | Nagelkerke's R2
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#> ------------------------------
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#> m1 | |
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#> m2 | |
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#> m3 | 0.722 |
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#> m4 | | 0.657
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```
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#### General index of model performance
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``` r
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#> # Comparison of Model Performance Indices
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#> Name | Model | RMSE | Sigma | AIC weights | AICc weights | BIC weights | Performance-Score
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#> -----------------------------------------------------------------------------------------------
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#> m2 | glm | 0.359 | 1.000 | 1.000 | 1.000 | 1.000 | 100.00%
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#> Name | Model | BF | Omega2 | p (Omega2) | LR | p (LR)
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#> ------------------------------------------------------------
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#> lm1 | lm | | | | |
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#> lm2 | lm | > 1000 | 0.69 | < .001 | -6.25 | < .001
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#> lm3 | lm | > 1000 | 0.36 | < .001 | -3.44 | < .001
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#> lm4 | lm | > 1000 | 0.73 | < .001 | -7.77 | < .001
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#> Each model is compared to lm1.
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test_bf(lm1, lm2, lm3, lm4)
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#> Bayes Factors for Model Comparison
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#> Model BF
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#> [lm2] Species + Petal.Length 3.45e+26
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#> [lm3] Species * Sepal.Width 4.69e+07
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#> [lm4] Species * Sepal.Width + Petal.Length + Petal.Width 7.58e+29
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#> * Against Denominator: [lm1] Species
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#> * Bayes Factor Type: BIC approximation
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```

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