General
- Removed suggested packages that have been removed from CRAN.
Changes to functions
icc() now also computes a “classical” ICC for brmsfit models. The former way of calculating an “ICC” for brmsfit models is now available as new function called variance_decomposition().
Bug fixes
- Fix issue with new version of bigutilsr for
check_outliers().
- Fix issue with model order in
performance_lrt().
General
- Support for models from package mfx.
Changes to functions
model_performance.rma() now includes results from heterogeneity test for meta-analysis objects.check_normality() now also works for mixed models (with the limitation that studentized residuals are used).check_normality() gets an effects-argument for mixed models, to check random effects for normality.
Bug fixes
- Fixed issue in
performance_accuracy() for binomial models when response variable had non-numeric factor levels.
- Fixed issues in
performance_roc(), which printed 1 - AUC instead of AUC.
General
- Minor revisions to
model_performance() to meet changes in mlogit package.
- Support for
bayesx models.
Changes to functions
icc() gains a by_group argument, to compute ICCs per different group factors in mixed models with multiple levels or cross-classified design.r2_nakagawa() gains a by_group argument, to compute explained variance at different levels (following the variance-reduction approach by Hox 2010).performance_lrt() now works on lavaan objects.
Bug fixes
- Fix issues in some functions for models with logical dependent variable.
- Fix bug in
check_itemscale(), which caused multiple computations of skewness statistics.
- Fix issues in
r2() for gam models.
General
model_performance() and r2() now support rma-objects from package metafor, mlm and bife models.
Changes to functions
compare_performance() gets a bayesfactor argument, to include or exclude the Bayes factor for model comparisons in the output.- Added
r2.aov().
Bug fixes
- Fixed issue in
performance_aic() for models from package survey, which returned three different AIC values. Now only the AIC value is returned.
- Fixed issue in
check_collinearity() for glmmTMB models when zero-inflated formula only had one predictor.
- Fixed issue in
check_model() for lme models.
- Fixed issue in
check_distribution() for brmsfit models.
- Fixed issue in
check_heteroscedasticity() for aov objects.
- Fixed issues for lmrob and glmrob objects.
General
- Removed
logLik.felm(), because this method is now implemented in the lfe package.
- Support for
DirichletRegModel models.
New functions
check_itemscale() to describe various measures of internal consistencies for scales which were built from several items from a PCA, using parameters::principal_components().r2_efron() to compute Efron’s pseudo R2.
Bug fixes
- Fixed issue in documentation of
performance_score().
General
- Support for
mixor, cpglm and cpglmm models.
New functions
performance_aic() as a small wrapper that returns the AIC. It is a generic function that also works for some models that don’t have a AIC method (like Tweedie models).performance_lrt() as a small wrapper around anova() to perform a Likelihood-Ratio-Test for model comparison.
Bug fixes
- Fix issues with CRAN checks.
Changes to functions
model_performance() now calculates AIC for Tweedie models.
General
- Support for
bracl, brmultinom, fixest, glmx, glmmadmb, mclogit, mmclogit, vgam and vglm models.
model_performance() now supports plm models.r2() now supports complmrob models.compare_performance() now gets a plot()-method (requires package see).
Changes to functions
compare_performance() gets a rank-argument, to rank models according to their overall model performance.compare_performance() has a nicer print()-method now.- Verbosity for
compare_performance() was slightly adjusted.
model_performance()-methods for different objects now also have a verbose-argument.
Minor changes
check_collinearity() now no longer returns backticks in row- and column names.
Bug fixes
- Fixed issue in
r2() for wbm-models with cross-level interactions.
plot()-methods for check_heteroscedasticity() and check_homogeneity() now work without requiring to load package see before.- Fixed issues with models of class
rlmerMod.
General
performance() is an alias for model_performance().
Deprecated and Defunct
principal_components() was removed and re-implemented in the parameters-package. Please use parameters::principal_components() now.
Changes to functions
check_outliers() now also works on data frames.- Added more methods to
check_outliers().
performance_score() now also works on stan_lmer() and stan_glmer() objects.check_singularity() now works with models of class clmm.r2() now works with models of class clmm, bigglm and biglm.check_overdispersion() for mixed models now checks that model family is Poisson.
Bug fixes
- Fixed bug in
compare_performance() that toggled a warning although models were fit from same data.
- Fixed bug in
check_model() for glmmTMB models that occurred when checking for outliers.
General
- Many
check_*()-methods now get a plot()-method. Package see is required for plotting.
model_performance() gets a preliminary print()-method.
Breaking changes
- The attribute for the standard error of the Bayesian R2 (
r2_bayes()) was renamed from std.error to SE to be in line with the naming convention of other easystats-packages.
compare_performance() now shows the Bayes factor when all compared models are fit from the same data. Previous behaviour was that the BF was shown when models were of same class.
Changes to functions
model_performance() now also works for lavaan-objects.check_outliers() gets a method-argument to choose the method for detecting outliers. Furthermore, two new methods (Mahalanobis Distance and Invariant Coordinate Selection) were implemented.check_model() now performs more checks for GLM(M)s and other model objects.check_model() gets a check-argument to plot selected checks only.r2_nakagawa() now returns r-squared for models with singular fit, where no random effect variances could be computed. The r-squared then does not take random effect variances into account. This behaviour was changed to be in line with MuMIn::r.squaredGLMM(), which returned a value for models with singular fit.check_distribution() now detects negative binomial and zero-inflated distributions. Furthermore, attempt to improve accuracy.check_distribution() now also accepts a numeric vector as input.compare_performance() warns if models were not fit from same data.
New check-functions
check_homogeneity() to check models for homogeneity of variances.
Bug fixes
- Fixed issues with
compare_performance() and row-ordering.
- Fixed issue in
check_collinearity() for zero-inflated models, where the zero-inflation component had not enough model terms to calculate multicollinearity.
- Fixed issue in some
check_*() and performance_*() functions for models with binary outcome, when outcome variable was a factor.
General
r2() now works for more regression models.r2_bayes() now works for multivariate response models.model_performance() now works for more regression models, and also includes the log-loss, proper scoring rules and percentage of correct predictions as new metric for models with binary outcome.
performance_accuracy(), which calculates the predictive accuracy of linear or logistic regression models.performance_logloss() to compute the log-loss of models with binary outcome. The log-loss is a proper scoring function comparable to the rmse().performance_score() to compute the logarithmic, quadratic and spherical proper scoring rules.performance_pcp() to calculate the percentage of correct predictions for models with binary outcome.performance_roc(), to calculate ROC-curves.performance_aicc(), to calculate the second-order AIC (AICc).
New check-functions
check_collinearity() to calculate the variance inflation factor and check model predictors for multicollinearity.check_outliers() to check models for influential observations.check_heteroscedasticity() to check models for (non-)constant error variance.check_normality() to check models for (non-)normality of residuals.check_autocorrelation() to check models for auto-correlated residuals.check_distribution() to classify the distribution of a model-family using machine learning.
New indices-functions
r2_mckelvey() to compute McKelvey and Zavoinas R2 value.r2_zeroinflated() to compute R2 for zero-inflated (non-mixed) models.r2_xu() as a crude R2 measure for linear (mixed) models.
Breaking changes
model_performance.stanreg() and model_performance.brmsfit() now only return one R2-value and its standard error, instead of different (robust) R2 measures and credible intervals.error_rate() is now integrated in the performance_pcp()-function.
Changes to functions
model_performance.stanreg() and model_performance.brmsfit() now also return the WAIC (widely applicable information criterion).r2_nakagawa() now calculates the full R2 for mixed models with zero-inflation.icc() now returns NULL and no longer stops when no mixed model is provided.compare_performance() now shows the Bayes factor when all compared models are of same class.- Some functions get a
verbose-argument to show or suppress warnings.
Bug fixes
- Renamed
r2_coxnell() to r2_coxsnell().
- Fix issues in
r2_bayes() and model_performance() for ordinal models resp. models with cumulative link (#48).
compare_performance() did not sort the name-column properly, if the columns class and name were not in the same alphabetical order (#51).