Package index • performance

Comprehensive Summaries

model_performance() performance(): Model Performance
model_performance(<fa>): Performance of FA / PCA models
model_performance(<ivreg>): Performance of instrumental variable regression models
model_performance(<kmeans>): Model summary for k-means clustering
model_performance(<lavaan>): Performance of lavaan SEM / CFA Models
model_performance(<lm>): Performance of Regression Models
model_performance(<merMod>): Performance of Mixed Models
model_performance(<rma>): Performance of Meta-Analysis Models
model_performance(<stanreg>) model_performance(<BFBayesFactor>): Performance of Bayesian Models

Check Model Assumptions or Data Properties

binned_residuals(): Binned residuals for binomial logistic regression
check_autocorrelation(): Check model for independence of residuals.
check_clusterstructure(): Check suitability of data for clustering
check_collinearity() multicollinearity() check_concurvity(): Check for multicollinearity of model terms
check_convergence(): Convergence test for mixed effects models
check_dag() as.dag(): Check correct model adjustment for identifying causal effects
check_distribution(): Classify the distribution of a model-family using machine learning
check_factorstructure() check_kmo() check_sphericity_bartlett(): Check suitability of data for Factor Analysis (FA) with Bartlett's Test of Sphericity and KMO
check_group_variation() summary(<check_group_variation>): Check variables for within- and/or between-group variation
check_heterogeneity_bias(): Check model predictor for heterogeneity bias (Deprecated)
check_heteroscedasticity() check_heteroskedasticity(): Check model for (non-)constant error variance
check_homogeneity(): Check model for homogeneity of variances
check_itemscale(): Describe Properties of Item Scales
check_model(): Visual check of model assumptions
check_multimodal(): Check if a distribution is unimodal or multimodal
check_normality(): Check model for (non-)normality of residuals.
check_outliers(): Outliers detection (check for influential observations)
check_overdispersion(): Check overdispersion (and underdispersion) of GL(M)M's
check_predictions(): Posterior predictive checks
check_residuals(): Check distribution of simulated quantile residuals
check_singularity(): Check mixed models for boundary fits
check_sphericity(): Check model for violation of sphericity
check_symmetry(): Check distribution symmetry
check_zeroinflation(): Check for zero-inflation in count models
simulate_residuals() residuals(<performance_simres>): Simulate randomized quantile residuals from a model

Check Model Performance or Quality

performance_accuracy(): Accuracy of predictions from model fit
performance_aicc() performance_aic(): Compute the AIC or second-order AIC
performance_cv(): Cross-validated model performance
performance_hosmer(): Hosmer-Lemeshow goodness-of-fit test
performance_logloss(): Log Loss
performance_mae() mae(): Mean Absolute Error of Models
performance_mse() mse(): Mean Square Error of Linear Models
performance_pcp(): Percentage of Correct Predictions
performance_reliability() performance_dvour(): Random Effects Reliability
performance_rmse() rmse(): Root Mean Squared Error
performance_roc(): Simple ROC curve
performance_rse(): Residual Standard Error for Linear Models
performance_score(): Proper Scoring Rules
icc() variance_decomposition(): Intraclass Correlation Coefficient (ICC)
looic(): LOO-related Indices for Bayesian regressions.

Check Items and Scales

check_itemscale(): Describe Properties of Item Scales
cronbachs_alpha() item_alpha(): Cronbach's Alpha for Items or Scales
item_difficulty(): Difficulty of Questionnaire Items
item_discrimination() item_totalcor(): Discrimination and Item-Total Correlation of Questionnaire Items
item_intercor(): Mean Inter-Item-Correlation
item_omega(): McDonald's Omega for Items or Scales
item_reliability(): Reliability Test for Items or Scales
item_split_half(): Split-Half Reliability

Comparing and Testing Models

compare_performance(): Compare performance of different models
test_bf() test_likelihoodratio() test_lrt() test_performance() test_vuong() test_wald(): Test if models are different

R2 Functions

r2(): Compute the model's R2
r2_bayes() r2_posterior(): Bayesian R2
r2_coxsnell(): Cox & Snell's R2
r2_efron(): Efron's R2
r2_ferrari(): Ferrari's and Cribari-Neto's R2
r2_kullback(): Kullback-Leibler R2
r2_loo() r2_loo_posterior(): LOO-adjusted R2
r2_mcfadden(): McFadden's R2
r2_mckelvey(): McKelvey & Zavoinas R2
r2_mlm(): Multivariate R2
r2_nagelkerke(): Nagelkerke's R2
r2_nakagawa(): Nakagawa's R2 for mixed models
r2_somers(): Somers' Dxy rank correlation for binary outcomes
r2_tjur(): Tjur's R2 - coefficient of determination (D)
r2_xu(): Xu' R2 (Omega-squared)
r2_zeroinflated(): R2 for models with zero-inflation

Table Formatting

display(<performance_model>) print(<performance_model>) print_md(<performance_model>) print_md(<compare_performance>): Print tables in different output formats
reexports display print_md print_html: Objects exported from other packages

Sample Data

classify_distribution: Classify the distribution of a model-family using machine learning