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_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