Assessment of Regression Models Performance
Binned residuals for binomial logistic regression
Check model for independence of residuals.
Check suitability of data for clustering
Check for multicollinearity of model terms
Convergence test for mixed effects models
Check correct model adjustment for identifying causal effects
Classify the distribution of a model-family using machine learning
Check suitability of data for Factor Analysis (FA) with Bartlett's Tes...
Check model predictor for heterogeneity bias
Check model for (non-)constant error variance
Check model for homogeneity of variances
Describe Properties of Item Scales
Visual check of model assumptions
Check if a distribution is unimodal or multimodal
Check model for (non-)normality of residuals.
Outliers detection (check for influential observations)
Check overdispersion (and underdispersion) of GL(M)M's
Posterior predictive checks
Check uniformity of simulated residuals
Check mixed models for boundary fits
Check model for violation of sphericity
Check distribution symmetry
Check for zero-inflation in count models
Classify the distribution of a model-family using machine learning
Compare performance of different models
Cronbach's Alpha for Items or Scales
Print tables in different output formats
Intraclass Correlation Coefficient (ICC)
Difficulty of Questionnaire Items
Discrimination of Questionnaire Items
Mean Inter-Item-Correlation
Reliability Test for Items or Scales
Split-Half Reliability
LOO-related Indices for Bayesian regressions.
Performance of instrumental variable regression models
Model summary for k-means clustering
Performance of lavaan SEM / CFA Models
Performance of Regression Models
Performance of Mixed Models
Model Performance
Performance of Meta-Analysis Models
Performance of Bayesian Models
Accuracy of predictions from model fit
Compute the AIC or second-order AIC
Cross-validated model performance
Hosmer-Lemeshow goodness-of-fit test
Log Loss
Mean Absolute Error of Models
Mean Square Error of Linear Models
Percentage of Correct Predictions
Root Mean Squared Error
Simple ROC curve
Residual Standard Error for Linear Models
Proper Scoring Rules
performance: An R Package for Assessment, Comparison and Testing of St...
Bayesian R2
Cox & Snell's R2
Efron's R2
Ferrari's and Cribari-Neto's R2
Kullback-Leibler R2
LOO-adjusted R2
McFadden's R2
McKelvey & Zavoinas R2
Multivariate R2
Nagelkerke's R2
Nakagawa's R2 for mixed models
Somers' Dxy rank correlation for binary outcomes
Tjur's R2 - coefficient of determination (D)
Xu' R2 (Omega-squared)
R2 for models with zero-inflation
Compute the model's R2
Objects exported from other packages
Simulate randomized quantile residuals from a model
Test if models are different
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>.
Useful links