performance: An R Package for Assessment, Comparison and Testing of Statistical Models
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Abstract
A crucial part of statistical analysis is evaluating a model's quality and fit, or performance.During analysis, especially with regression models, investigating the fit of models to data also often involves selecting the best fitting model amongst many competing models.Upon investigation, fit indices should also be reported both visually and numerically to bring readers in on the investigative effort.The performance R-package (R Core Team, 2021) provides utilities for computing measures to assess model quality, many of which are not directly provided by R's base or stats packages.These include measures like R 2 , intraclass correlation coefficient (ICC), root mean squared error (RMSE), or functions to check for vexing issues like overdispersion, singularity, or zeroinflation.These functions support a large variety of regression models including generalized linear models, (generalized) mixed-effects models, their Bayesian cousins, and many others. Statement of NeedWhile functions to build and produce diagnostic plots or to compute fit statistics exist, these are located across many packages, which results in a lack of a unique and consistent approach to assess the performance of many types of models.The result is a difficult-to-navigate, unorganized ecosystem of individual packages with different syntax, making it onerous for researchers to locate and use fit indices relevant for their unique purposes.The performance package in R fills this gap by offering researchers a suite of intuitive functions with consistent syntax for computing, building, and presenting regression model fit statistics and visualizations.performance is part of the easystats ecosystem, which is a collaborative project focused on facilitating simple and intuitive usage of R for statistical analysis (Ben-Shachar et al.,
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The residuals of all models were checked for normality, homogeneity of variances, and outliers; BHB, NEFA, urea, CK, and cortisol were log-transformed to meet model assumptions; marginal and conditional R2 were calculated; fixed effects and interactions were tested for significance (P < 0.05) using ANOVA with approximate F-tests; for significant effects, estimated marginal means and pairwise
“The residuals of all models were checked for normality, homogeneity of variances, and outliers with the package ‘performance’ version 0.13.0 [37]. The variables BHB, NEFA, urea, CK, and cortisol were log-transformed to meet model assumptions. To assess model fit and the amount of variance explained by the fixed effects, we calculated marginal and conditional R2 after Nakagawa and Schielzeth [38]”
From Effects of age and feeding protocols on the metabolic and physiological condition of unweaned calves during and after long-distance transport (2026) · cites this paper as [37]
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Research works printed but not linked: 2
- Lüdecke, (2021). performance: An R Package for Assessment, Comparison and Testing of Statistical Models. Journal of Open Source Software, 6(60), 3139. https://doi.org/10.21105/joss.03139
- Lüdecke, (2021). performance: An R Package for Assessment, Comparison and Testing of Statistical Models. Journal of Open Source Software, 6(60), 3139. https://doi.org/10.21105/joss.03139
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- Authors
- Lüdecke, D., Ben-Shachar, M., Patil, I., Waggoner, P., & Makowski, D.
- Affiliations
- University Medical Center Hamburg-Eppendorf (DE)
- Max Planck Institute for Human Development (DE)
- University of Chicago (US)
- Nanyang Technological University (SG)
- Venue
- Journal of Open Source Software
- Publisher
- The Open Journal
- Published
- 2021-04-21
- Volume
- 6
- Issue
- 60
- Pages
- 3139
- ISSN
- 2475-9066 (ISSN-L)
- OpenAlex
- W3153999239
- Cited by
- 5,836
- References
- 12
- Citations by year
- 2026: 1676 · 2025: 1511 · 2024: 1211 · 2023: 806 · 2022: 519 · 2021: 106 · 2020: 3
- FWCI
- 233.93
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- Locations
- The Journal of Open Source Software (publishedVersion, cc-by)
- MPG.PuRe (Max Planck Society) (submittedVersion, other-oa)
- Sussex Research Online (University of Sussex) (submittedVersion, cc-by)
- Figshare (submittedVersion, cc-by)
- PsyArXiv (OSF Preprints) (submittedVersion, cc-by)
- PsyArXiv (OSF Preprints) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Data Analysis with R
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Statistical Methods and Models
- Keywords
- model fit statistics, R package, overdispersion, singularities, excess zeros, generalized linear models, mixed models, Bayesian model, R-squared, intraclass correlation coefficient, root mean square error, model comparison
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