Patch release, to ensure that *performance* runs with older
version of *datawizard* on Mac OSX with R (old-release).

`icc()`

and`r2_nakagawa()`

get a`null_model`

argument. This can be useful when computing R2 or ICC for mixed models, where the internal computation of the null model fails, or when you already have fit the null model and want to save time.`icc()`

and`r2_nakagawa()`

get a`approximation`

argument indicating the approximation method for the distribution-specific (residual) variance. See Nakagawa et al. 2017 for details.`icc()`

and`r2_nakagawa()`

get a`model_component`

argument indicating the component for zero-inflation or hurdle models.`performance_rmse()`

(resp.`rmse()`

) can now compute analytical and bootstrapped confidence intervals. The function gains following new arguments:`ci`

,`ci_method`

and`iterations`

.New function

`r2_ferrari()`

to compute Ferrari & Cribari-Neto’s R2 for generalized linear models, in particular beta-regression.Improved documentation of some functions.

Fixed issue in

`check_model()`

when model contained a transformed response variable that was named like a valid R function name (e.g.,`lm(log(lapply) ~ x)`

, when data contained a variable named`lapply`

).Fixed issue in

`check_predictions()`

for linear models when response was transformed as ratio (e.g.`lm(succes/trials ~ x)`

).Fixed issue in

`r2_bayes()`

for mixed models from*rstanarm*.

Aliases

`posterior_predictive_check()`

and`check_posterior_predictions()`

for`check_predictions()`

are deprecated.Arguments named

`group`

or`group_by`

will be deprecated in a future release. Please use`by`

instead. This affects`check_heterogeneity_bias()`

in*performance*.

Improved documentation and new vignettes added.

`check_model()`

gets a`base_size`

argument, to set the base font size for plots.`check_predictions()`

for`stanreg`

and`brmsfit`

models now returns plots in the usual style as for other models and no longer returns plots from`bayesplot::pp_check()`

.Updated the trained model that is used to prediction distributions in

`check_distribution()`

.

`check_model()`

now falls back on normal Q-Q plots when a model is not supported by the DHARMa package and simulated residuals cannot be calculated.

- Rudimentary support for models of class
`serp`

from package*serp*.

`simulate_residuals()`

and`check_residuals()`

, to simulate and check residuals from generalized linear (mixed) models. Simulating residuals is based on the DHARMa package, and objects returned by`simulate_residuals()`

inherit from the`DHARMa`

class, and thus can be used with any functions from the*DHARMa*package. However, there are also implementations in the*performance*package, such as`check_overdispersion()`

,`check_zeroinflation()`

,`check_outliers()`

or`check_model()`

.Plots for

`check_model()`

have been improved. The Q-Q plots are now based on simulated residuals from the DHARMa package for non-Gaussian models, thus providing more accurate and informative plots. The half-normal QQ plot for generalized linear models can still be obtained by setting the new argument`residual_type = "normal"`

.Following functions now support simulated residuals (from

`simulate_residuals()`

) resp. objects returned from`DHARMa::simulateResiduals()`

:`check_overdispersion()`

`check_zeroinflation()`

`check_outliers()`

`check_model()`

Improved error messages for

`check_model()`

when QQ-plots cannot be created.`check_distribution()`

is more stable for possibly sparse data.

Fixed issue in

`check_normality()`

for t-tests.Fixed issue in

`check_itemscale()`

for data frame inputs, when`factor_index`

was not a named vector.

`r2()`

for models of class`glmmTMB`

without random effects now returns the correct r-squared value for non-mixed models.`check_itemscale()`

now also accepts data frames as input. In this case,`factor_index`

must be specified, which must be a numeric vector of same length as number of columns in`x`

, where each element is the index of the factor to which the respective column in`x`

.`check_itemscale()`

gets a`print_html()`

method.Clarification in the documentation of the

`estimator`

argument for`performance_aic()`

.Improved plots for overdispersion-checks for negative-binomial models from package

*glmmTMB*(affects`check_overdispersion()`

and`check_model()`

).Improved detection rates for singularity in

`check_singularity()`

for models from package*glmmTMB*.For model of class

`glmmTMB`

, deviance residuals are now used in the`check_model()`

plot.Improved (better to understand) error messages for

`check_model()`

,`check_collinearity()`

and`check_outliers()`

for models with non-numeric response variables.`r2_kullback()`

now gives an informative error for non-supported models.

Fixed issue in

`binned_residuals()`

for models with binary outcome, where in rare occasions empty bins could occur.`performance_score()`

should no longer fail for models where scoring rules can’t be calculated. Instead, an informative message is returned.`check_outliers()`

now properly accept the`percentage_central`

argument when using the`"mcd"`

method.Fixed edge cases in

`check_collinearity()`

and`check_outliers()`

for models with response variables of classes`Date`

,`POSIXct`

,`POSIXlt`

or`difftime`

.Fixed issue with

`check_model()`

for models of package*quantreg*.

- Changed behaviour of
`check_predictions()`

for models from binomial family, to get comparable plots for different ways of outcome specification. Now, if the outcome is a proportion, or defined as matrix of trials and successes, the produced plots are the same (because the models should be the same, too).

Fixed CRAN check errors.

Fixed issue with

`binned_residuals()`

for models with binomial family, where the outcome was a proportion.

`binned_residuals()`

gains a few new arguments to control the residuals used for the test, as well as different options to calculate confidence intervals (namely,`ci_type`

,`residuals`

,`ci`

and`iterations`

). The default values to compute binned residuals have changed. Default residuals are now “deviance” residuals (and no longer “response” residuals). Default confidence intervals are now “exact” intervals (and no longer based on Gaussian approximation). Use`ci_type = "gaussian"`

and`residuals = "response"`

to get the old defaults.

`binned_residuals()`

- like`check_model()`

- gains a`show_dots`

argument to show or hide data points that lie inside error bounds. This is particular useful for models with many observations, where generating the plot would be very slow.

- Support for
`nestedLogit`

models.

`check_outliers()`

for method`"ics"`

now detects number of available cores for parallel computing via the`"mc.cores"`

option. This is more robust than the previous method, which used`parallel::detectCores()`

. Now you should set the number of cores via`options(mc.cores = 4)`

.

- Fixed issues is
`check_model()`

for models that used data sets with variables of class`"haven_labelled"`

.

More informative message for

`test_*()`

functions that “nesting” only refers to fixed effects parameters and currently ignores random effects when detecting nested models.`check_outliers()`

for`"ICS"`

method is now more stable and less likely to fail.`check_convergence()`

now works for*parsnip*`_glm`

models.

`check_collinearity()`

did not work for hurdle- or zero-inflated models of package*pscl*when model had no explicitly defined formula for the zero-inflation model.

`icc()`

and`r2_nakagawa()`

gain a`ci_method`

argument, to either calculate confidence intervals using`boot::boot()`

(instead of`lmer::bootMer()`

) when`ci_method = "boot"`

or analytical confidence intervals (`ci_method = "analytical"`

). Use`ci_method = "boot"`

when the default method fails to compute confidence intervals and use`ci_method = "analytical"`

if bootstrapped intervals cannot be calculated at all. Note that the default computation method is preferred.`check_predictions()`

accepts a`bandwidth`

argument (smoothing bandwidth), which is passed down to the`plot()`

methods density-estimation.`check_predictions()`

gains a`type`

argument, which is passed down to the`plot()`

method to change plot-type (density or discrete dots/intervals). By default,`type`

is set to`"default"`

for models without discrete outcomes, and else`type = "discrete_interval"`

.`performance_accuracy()`

now includes confidence intervals, and reports those by default (the standard error is no longer reported, but still included).

- Fixed issue in
`check_collinearity()`

for*fixest*models that used`i()`

to create interactions in formulas.

`item_discrimination()`

, to calculate the discrimination of a scale’s items.

`model_performance()`

,`check_overdispersion()`

,`check_outliers()`

and`r2()`

now work with objects of class`fixest_multi`

(@etiennebacher, #554).`model_performance()`

can now return the “Weak instruments” statistic and p-value for models of class`ivreg`

with`metrics = "weak_instruments"`

(@etiennebacher, #560).Support for

`mclogit`

models.

`test_*()`

functions now automatically fit a null-model when only one model objects was provided for testing multiple models.Warnings in

`model_performance()`

for unsupported objects of class`BFBayesFactor`

can now be suppressed with`verbose = FALSE`

.`check_predictions()`

no longer fails with issues when`re_formula = NULL`

for mixed models, but instead gives a warning and tries to compute posterior predictive checks with`re_formuka = NA`

.`check_outliers()`

now also works for meta-analysis models from packages*metafor*and*meta*.`plot()`

for`performance::check_model()`

no longer produces a normal QQ plot for GLMs. Instead, it now shows a half-normal QQ plot of the absolute value of the standardized deviance residuals.

- Fixed issue in
`print()`

method for`check_collinearity()`

, which could mix up the correct order of parameters.

- Revised usage of
`insight::get_data()`

to meet forthcoming changes in the*insight*package.

`check_collinearity()`

now accepts`NULL`

for the`ci`

argument.

- Fixed issue in
`item_difficulty()`

with detecting the maximum values of an item set. Furthermore,`item_difficulty()`

gets a`maximum_value`

argument in case no item contains the maximum value due to missings.

- Minor improvements to the documentation.

`icc()`

and`r2_nakagawa()`

get`ci`

and`iterations`

arguments, to compute confidence intervals for the ICC resp. R2, based on bootstrapped sampling.`r2()`

gets`ci`

, to compute (analytical) confidence intervals for the R2.The model underlying

`check_distribution()`

was now also trained to detect cauchy, half-cauchy and inverse-gamma distributions.`model_performance()`

now allows to include the ICC for Bayesian models.

`verbose`

didn’t work for`r2_bayes()`

with`BFBayesFactor`

objects.Fixed issues in

`check_model()`

for models with convergence issues that lead to`NA`

values in residuals.Fixed bug in

`check_outliers`

whereby passing multiple elements to the threshold list generated an error (#496).`test_wald()`

now warns the user about inappropriate F test and calls`test_likelihoodratio()`

for binomial models.Fixed edge case for usage of

`parellel::detectCores()`

in`check_outliers()`

.

The minimum needed R version has been bumped to

`3.6`

.The alias

`performance_lrt()`

was removed. Use`test_lrt()`

resp.`test_likelihoodratio()`

.

- Following functions were moved from package
*parameters*to*performance*:`check_sphericity_bartlett()`

,`check_kmo()`

,`check_factorstructure()`

and`check_clusterstructure()`

.

`check_normality()`

,`check_homogeneity()`

and`check_symmetry()`

now works for`htest`

objects.Print method for

`check_outliers()`

changed significantly: now states the methods, thresholds, and variables used, reports outliers per variable (for univariate methods) as well as any observation flagged for several variables/methods. Includes a new optional ID argument to add along the row number in the output (@rempsyc #443).`check_outliers()`

now uses more conventional outlier thresholds. The`IQR`

and confidence interval methods now gain improved distance scores that are continuous instead of discrete.

Fixed wrong

*z*-score values when using a vector instead of a data frame in`check_outliers()`

(#476).Fixed

`cronbachs_alpha()`

for objects from`parameters::principal_component()`

.

`print()`

methods for`model_performance()`

and`compare_performance()`

get a`layout`

argument, which can be`"horizontal"`

(default) or`"vertical"`

, to switch the layout of the printed table.Improved speed performance for

`check_model()`

and some other`performance_*()`

functions.Improved support for models of class

`geeglm`

.

`check_model()`

gains a`show_dots`

argument, to show or hide data points. This is particular useful for models with many observations, where generating the plot would be very slow.

- Fixes wrong column names in
`model_performance()`

output for`kmeans`

objects (#453)

- The formerly “conditional” ICC in
`icc()`

is now named “unadjusted” ICC.

`performance_cv()`

for cross-validated model performance.

- Added support for models from package
*estimator*.

`check_overdispersion()`

gets a`plot()`

method.`check_outliers()`

now also works for models of classes`gls`

and`lme`

. As a consequence,`check_model()`

will no longer fail for these models.`check_collinearity()`

now includes the confidence intervals for the VIFs and tolerance values.`model_performance()`

now also includes within-subject R2 measures, where applicable.Improved handling of random effects in

`check_normality()`

(i.e. when argument`effects = "random"`

).

`check_predictions()`

did not work for GLMs with matrix-response.`check_predictions()`

did not work for logistic regression models (i.e. models with binary response) from package*glmmTMB*`item_split_half()`

did not work when the input data frame or matrix only contained two columns.Fixed wrong computation of

`BIC`

in`model_performance()`

when models had transformed response values.Fixed issues in

`check_model()`

for GLMs with matrix-response.

`check_concurvity()`

, which returns GAM concurvity measures (comparable to collinearity checks).

`check_predictions()`

,`check_collinearity()`

and`check_outliers()`

now support (mixed) regression models from`BayesFactor`

.`check_zeroinflation()`

now also works for`lme4::glmer.nb()`

models.`check_collinearity()`

better supports GAM models.

`test_performance()`

now calls`test_lrt()`

or`test_wald()`

instead of`test_vuong()`

when package*CompQuadForm*is missing.`test_performance()`

and`test_lrt()`

now compute the corrected log-likelihood when models with transformed response variables (such as log- or sqrt-transformations) are passed to the functions.

`performance_aic()`

now corrects the AIC value for models with transformed response variables. This also means that comparing models using`compare_performance()`

allows comparisons of AIC values for models with and without transformed response variables.Also,

`model_performance()`

now corrects both AIC and BIC values for models with transformed response variables.

The

`print()`

method for`binned_residuals()`

now prints a short summary of the results (and no longer generates a plot). A`plot()`

method was added to generate plots.The

`plot()`

output for`check_model()`

was revised:For binomial models, the constant variance plot was omitted, and a binned residuals plot included.

The density-plot that showed normality of residuals was replaced by the posterior predictive check plot.

`model_performance()`

for models from*lme4*did not report AICc when requested.`r2_nakagawa()`

messed up order of group levels when`by_group`

was`TRUE`

.

The

`ci`

-level in`r2()`

for Bayesian models now defaults to`0.95`

, to be in line with the latest changes in the*bayestestR*package.S3-method dispatch for

`pp_check()`

was revised, to avoid problems with the*bayesplot*package, where the generic is located.

Minor revisions to wording for messages from some of the check-functions.

`posterior_predictive_check()`

and`check_predictions()`

were added as aliases for`pp_check()`

.

`check_multimodal()`

and`check_heterogeneity_bias()`

. These functions will be removed from the*parameters*packages in the future.

`r2()`

for linear models can now compute confidence intervals, via the`ci`

argument.

Fixed issues in

`check_model()`

for Bayesian models.Fixed issue in

`pp_check()`

for models with transformed response variables, so now predictions and observed response values are on the same (transformed) scale.

`check_outliers()`

has new`ci`

(or`hdi`

,`eti`

) method to filter based on Confidence/Credible intervals.`compare_performance()`

now also accepts a list of model objects.`performance_roc()`

now also works for binomial models from other classes than*glm*.Several functions, like

`icc()`

or`r2_nakagawa()`

, now have an`as.data.frame()`

method.`check_collinearity()`

now correctly handles objects from forthcoming*afex*update.

`performance_mae()`

to calculate the mean absolute error.

Fixed issue with

`"data length differs from size of matrix"`

warnings in examples in forthcoming R 4.2.Fixed issue in

`check_normality()`

for models with sample size larger than

5.000 observations.

Fixed issue in

`check_model()`

for*glmmTMB*models.Fixed issue in

`check_collinearity()`

for*glmmTMB*models with zero-inflation, where the zero-inflated model was an intercept-only model.

Add support for

`model_fit`

(*tidymodels*).`model_performance`

supports*kmeans*models.

Give more informative warning when

`r2_bayes()`

for*BFBayesFactor*objects can’t be calculated.Several

`check_*()`

functions now return informative messages for invalid model types as input.`r2()`

supports`mhurdle`

(*mhurdle*) models.Added

`print()`

methods for more classes of`r2()`

.The

`performance_roc()`

and`performance_accuracy()`

functions unfortunately had spelling mistakes in the output columns:*Sensitivity*was called*Sensivity*and*Specificity*was called*Specifity*. We think these are understandable mistakes :-)

`check_model()`

`check_model()`

gains more arguments, to customize plot appearance.Added option to detrend QQ/PP plots in

`check_model()`

.

`model_performance()`

The

`metrics`

argument from`model_performance()`

and`compare_performance()`

gains a`"AICc"`

option, to also compute the 2nd order AIC.`"R2_adj"`

is now an explicit option in the`metrics`

argument from`model_performance()`

and`compare_performance()`

.

The default-method for

`r2()`

now tries to compute an r-squared for all models that have no specific`r2()`

-method yet, by using following formula:`1-sum((y-y_hat)^2)/sum((y-y_bar)^2))`

The column name

`Parameter`

in`check_collinearity()`

is now more appropriately named`Term`

.

`test_likelihoodratio()`

now correctly sorts models with identical fixed effects part, but different other model parts (like zero-inflation).Fixed incorrect computation of models from inverse-Gaussian families, or Gaussian families fitted with

`glm()`

.Fixed issue in

`performance_roc()`

for models where outcome was not 0/1 coded.Fixed issue in

`performance_accuracy()`

for logistic regression models when`method = "boot"`

.`cronbachs_alpha()`

did not work for`matrix`

-objects, as stated in the docs. It now does.

- Roll-back R dependency to R >= 3.4.

`compare_performance()`

doesn’t return the models’ Bayes Factors, now returned by`test_performance()`

and`test_bf()`

.

`test_vuong()`

, to compare models using Vuong’s (1989) Test.`test_bf()`

, to compare models using Bayes factors.`test_likelihoodratio()`

as an alias for`performance_lrt()`

.`test_wald()`

, as a rough approximation for the LRT.`test_performance()`

, to run the most relevant and appropriate tests based on the input.

`performance_lrt()`

`performance_lrt()`

get an alias`test_likelihoodratio()`

.Does not return AIC/BIC now (as they are not related to LRT

*per se*and can be easily obtained with other functions).Now contains a column with the difference in degrees of freedom between models.

Fixed column names for consistency.

`model_performance()`

- Added more diagnostics to models of class
`ivreg`

.

Revised computation of

`performance_mse()`

, to ensure that it’s always based on response residuals.`performance_aic()`

is now more robust.

Fixed issue in

`icc()`

and`variance_decomposition()`

for multivariate response models, where not all model parts contained random effects.Fixed issue in

`compare_performance()`

with duplicated rows.`check_collinearity()`

no longer breaks for models with rank deficient model matrix, but gives a warning instead.Fixed issue in

`check_homogeneity()`

for`method = "auto"`

, which wrongly tested the response variable, not the residuals.Fixed issue in

`check_homogeneity()`

for edge cases where predictor had non-syntactic names.

`check_collinearity()`

gains a`verbose`

argument, to toggle warnings and messages.

- Fixed examples, now using suggested packages only conditionally.

`model_performance()`

now supports`margins`

,`gamlss`

,`stanmvreg`

and`semLme`

.

`r2_somers()`

, to compute Somers’ Dxy rank-correlation as R2-measure for logistic regression models.`display()`

, to print output from package-functions into different formats.`print_md()`

is an alias for`display(format = "markdown")`

.

`model_performance()`

`model_performance()`

is now more robust and doesn’t fail if an index could not be computed. Instead, it returns all indices that were possible to calculate.`model_performance()`

gains a default-method that catches all model objects not previously supported. If model object is also not supported by the default-method, a warning is given.`model_performance()`

for metafor-models now includes the degrees of freedom for Cochran’s Q.

`performance_mse()`

and`performance_rmse()`

now always try to return the (R)MSE on the response scale.`performance_accuracy()`

now accepts all types of linear or logistic regression models, even if these are not of class`lm`

or`glm`

.`performance_roc()`

now accepts all types of logistic regression models, even if these are not of class`glm`

.`r2()`

for mixed models and`r2_nakagawa()`

gain a`tolerance`

-argument, to set the tolerance level for singularity checks when computing random effect variances for the conditional r-squared.

Fixed issue in

`icc()`

introduced in the last update that make`lme`

-models fail.Fixed issue in

`performance_roc()`

for models with factors as response.

- Column names for
`model_performance()`

and`compare_performance()`

were changed to be in line with the*easystats*naming convention:`LOGLOSS`

is now`Log_loss`

,`SCORE_LOG`

is`Score_log`

and`SCORE_SPHERICAL`

is now`Score_spherical`

.

`r2_posterior()`

for Bayesian models to obtain posterior distributions of R-squared.

`r2_bayes()`

works with Bayesian models from`BayesFactor`

( #143 ).`model_performance()`

works with Bayesian models from`BayesFactor`

( #150 ).`model_performance()`

now also includes the residual standard deviation.Improved formatting for Bayes factors in

`compare_performance()`

.`compare_performance()`

with`rank = TRUE`

doesn’t use the`BF`

values when`BIC`

are present, to prevent “double-dipping” of the BIC values (#144).The

`method`

argument in`check_homogeneity()`

gains a`"levene"`

option, to use Levene’s Test for homogeneity.

- Fix bug in
`compare_performance()`

when`...`

arguments were function calls to regression objects, instead of direct function calls.

`r2()`

and`icc()`

support`semLME`

models (package*smicd*).`check_heteroscedasticity()`

should now also work with zero-inflated mixed models from*glmmTMB*and*GLMMadpative*.`check_outliers()`

now returns a logical vector. Original numerical vector is still accessible via`as.numeric()`

.

`pp_check()`

to compute posterior predictive checks for frequentist models.

Fixed issue with incorrect labeling of groups from

`icc()`

when`by_group = TRUE`

.Fixed issue in

`check_heteroscedasticity()`

for mixed models where sigma could not be calculated in a straightforward way.Fixed issues in

`check_zeroinflation()`

for`MASS::glm.nb()`

.Fixed CRAN check issues.

- Removed suggested packages that have been removed from CRAN.

`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()`

.

Fix issue with new version of

*bigutilsr*for`check_outliers()`

.Fix issue with model order in

`performance_lrt()`

.

- Support for models from package
*mfx*.

`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.

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.

Minor revisions to

`model_performance()`

to meet changes in*mlogit*package.Support for

`bayesx`

models.

`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.

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.

`model_performance()`

and`r2()`

now support*rma*-objects from package*metafor*,*mlm*and*bife*models.

`compare_performance()`

gets a`bayesfactor`

argument, to include or exclude the Bayes factor for model comparisons in the output.Added

`r2.aov()`

.

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.