Package: performance 0.12.4.6

Daniel Lüdecke

performance: Assessment of Regression Models Performance

Utilities for computing measures to assess model quality, which are not directly provided by R's 'base' or 'stats' packages. These include e.g. measures like r-squared, intraclass correlation coefficient (Nakagawa, Johnson & Schielzeth (2017) <doi:10.1098/rsif.2017.0213>), root mean squared error or functions to check models for overdispersion, singularity or zero-inflation and more. Functions apply to a large variety of regression models, including generalized linear models, mixed effects models and Bayesian models. References: Lüdecke et al. (2021) <doi:10.21105/joss.03139>.

Authors:Daniel Lüdecke [aut, cre], Dominique Makowski [aut, ctb], Mattan S. Ben-Shachar [aut, ctb], Indrajeet Patil [aut, ctb], Philip Waggoner [aut, ctb], Brenton M. Wiernik [aut, ctb], Rémi Thériault [aut, ctb], Vincent Arel-Bundock [ctb], Martin Jullum [rev], gjo11 [rev], Etienne Bacher [ctb], Joseph Luchman [ctb]

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performance/json (API)
NEWS

# Install 'performance' in R:
install.packages('performance', repos = c('https://easystats.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/easystats/performance/issues

On CRAN:

aiceasystatshacktoberfestloomachine-learningmixed-modelsmodelsperformancer2statistics

16.14 score 1.0k stars 45 packages 3.5k scripts 90k downloads 17 mentions 85 exports 3 dependencies

Last updated 3 hours agofrom:d07418e14b. Checks:OK: 7. Indexed: yes.

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Doc / VignettesOKNov 21 2024
R-4.5-winOKNov 21 2024
R-4.5-linuxOKNov 21 2024
R-4.4-winOKNov 21 2024
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R-4.3-winOKNov 21 2024
R-4.3-macOKNov 21 2024

Exports:as.dagbinned_residualscheck_autocorrelationcheck_clusterstructurecheck_collinearitycheck_concurvitycheck_convergencecheck_dagcheck_distributioncheck_factorstructurecheck_heterogeneity_biascheck_heteroscedasticitycheck_heteroskedasticitycheck_homogeneitycheck_itemscalecheck_kmocheck_modelcheck_multimodalcheck_normalitycheck_outlierscheck_overdispersioncheck_predictionscheck_residualscheck_singularitycheck_sphericitycheck_sphericity_bartlettcheck_symmetrycheck_zeroinflationcompare_performancecronbachs_alphadisplayiccitem_difficultyitem_discriminationitem_intercoritem_reliabilityitem_split_halflooicmaemodel_performancemsemulticollinearityperformanceperformance_accuracyperformance_aicperformance_aiccperformance_cvperformance_hosmerperformance_loglossperformance_maeperformance_mseperformance_pcpperformance_rmseperformance_rocperformance_rseperformance_scoreprint_htmlprint_mdr2r2_bayesr2_coxsnellr2_efronr2_ferrarir2_kullbackr2_loor2_loo_posteriorr2_mcfaddenr2_mckelveyr2_mlmr2_nagelkerker2_nakagawar2_posteriorr2_somersr2_tjurr2_xur2_zeroinflatedrmsesimulate_residualstest_bftest_likelihoodratiotest_lrttest_performancetest_vuongtest_waldvariance_decomposition

Dependencies:bayestestRdatawizardinsight

Readme and manuals

Help Manual

Help pageTopics
Binned residuals for binomial logistic regressionbinned_residuals
Check model for independence of residuals.check_autocorrelation check_autocorrelation.default
Check suitability of data for clusteringcheck_clusterstructure
Check for multicollinearity of model termscheck_collinearity check_collinearity.default check_collinearity.glmmTMB check_concurvity multicollinearity
Convergence test for mixed effects modelscheck_convergence
Check correct model adjustment for identifying causal effectsas.dag check_dag
Classify the distribution of a model-family using machine learningcheck_distribution
Check suitability of data for Factor Analysis (FA) with Bartlett's Test of Sphericity and KMOcheck_factorstructure check_kmo check_sphericity_bartlett
Check model predictor for heterogeneity biascheck_heterogeneity_bias
Check model for (non-)constant error variancecheck_heteroscedasticity check_heteroskedasticity
Check model for homogeneity of variancescheck_homogeneity check_homogeneity.afex_aov
Describe Properties of Item Scalescheck_itemscale
Visual check of model assumptionscheck_model check_model.default
Check if a distribution is unimodal or multimodalcheck_multimodal
Check model for (non-)normality of residuals.check_normality check_normality.merMod
Outliers detection (check for influential observations)check_outliers check_outliers.data.frame check_outliers.default check_outliers.numeric check_outliers.performance_simres
Check overdispersion (and underdispersion) of GL(M)M'scheck_overdispersion check_overdispersion.performance_simres
Posterior predictive checkscheck_predictions check_predictions.default
Check uniformity of simulated residualscheck_residuals check_residuals.default
Check mixed models for boundary fitscheck_singularity
Check model for violation of sphericitycheck_sphericity
Check distribution symmetrycheck_symmetry
Check for zero-inflation in count modelscheck_zeroinflation check_zeroinflation.default check_zeroinflation.performance_simres
Classify the distribution of a model-family using machine learningclassify_distribution
Compare performance of different modelscompare_performance
Cronbach's Alpha for Items or Scalescronbachs_alpha
Print tables in different output formatsdisplay.performance_model print_md.compare_performance print_md.performance_model
Intraclass Correlation Coefficient (ICC)icc variance_decomposition
Difficulty of Questionnaire Itemsitem_difficulty
Discrimination of Questionnaire Itemsitem_discrimination
Mean Inter-Item-Correlationitem_intercor
Reliability Test for Items or Scalesitem_reliability
Split-Half Reliabilityitem_split_half
LOO-related Indices for Bayesian regressions.looic
Model Performancemodel_performance performance
Performance of instrumental variable regression modelsmodel_performance.ivreg
Model summary for k-means clusteringmodel_performance.kmeans
Performance of lavaan SEM / CFA Modelsmodel_performance.lavaan
Performance of Regression Modelsmodel_performance.lm
Performance of Mixed Modelsmodel_performance.merMod
Performance of Meta-Analysis Modelsmodel_performance.rma
Performance of Bayesian Modelsmodel_performance.BFBayesFactor model_performance.stanreg
Accuracy of predictions from model fitperformance_accuracy
Compute the AIC or second-order AICperformance_aic performance_aic.default performance_aic.lmerMod performance_aicc
Cross-validated model performanceperformance_cv
Hosmer-Lemeshow goodness-of-fit testperformance_hosmer
Log Lossperformance_logloss
Mean Absolute Error of Modelsmae performance_mae
Mean Square Error of Linear Modelsmse performance_mse
Percentage of Correct Predictionsperformance_pcp
Root Mean Squared Errorperformance_rmse rmse
Simple ROC curveperformance_roc
Residual Standard Error for Linear Modelsperformance_rse
Proper Scoring Rulesperformance_score
Compute the model's R2r2 r2.default r2.merMod r2.mlm
Bayesian R2r2_bayes r2_posterior r2_posterior.BFBayesFactor r2_posterior.brmsfit r2_posterior.stanreg
Cox & Snell's R2r2_coxsnell
Efron's R2r2_efron
Ferrari's and Cribari-Neto's R2r2_ferrari r2_ferrari.default
Kullback-Leibler R2r2_kullback r2_kullback.glm
LOO-adjusted R2r2_loo r2_loo_posterior r2_loo_posterior.brmsfit r2_loo_posterior.stanreg
McFadden's R2r2_mcfadden
McKelvey & Zavoinas R2r2_mckelvey
Multivariate R2r2_mlm
Nagelkerke's R2r2_nagelkerke
Nakagawa's R2 for mixed modelsr2_nakagawa
Somers' Dxy rank correlation for binary outcomesr2_somers
Tjur's R2 - coefficient of determination (D)r2_tjur
Xu' R2 (Omega-squared)r2_xu
R2 for models with zero-inflationr2_zeroinflated
Simulate randomized quantile residuals from a modelresiduals.performance_simres simulate_residuals
Test if models are differenttest_bf test_bf.default test_likelihoodratio test_lrt test_performance test_vuong test_wald