performance - performance 0.15.1

Changes

• display() now supports the tinytable format, when format = "tt".

• Better handling of non-converged lavaan-models in model_performance().

Scientific Software - Peer-reviewed - R
Published by strengejacke 4 months ago

performance - performance 0.15.0

New functions

• item_omega(), to calculate the McDonald's Omega reliability coefficient.

• item_totalcor() calculates the total correlation of an item with the sum of all other items in a scale. If corrected = TRUE, the total correlation is corrected for the number of items in the scale (which is equivalent to item_discrimination()).

• Column names of item_reliability() were changed to be in line with the easystats naming convention and to be consistent with the output of other related functions.

Changes

• check_itemscale() now work with factor analysis results, from parameters::factor_analysis().

• item_reliability() now includes the item-total correlation, and information about Cronbach's alpha and mean inter-item correlation in the printed output.

• cronbachs_alpha() now work with factor analysis results, from parameters::factor_analysis().

• Formatting of p-values in test_likelihoodratio() is now consistent with formatted p-values from other functions.

• Added following methods for psych::fa(), psych::principal(), item_omega(), psych::omega(), and parameters::factor_analysis(): check_normality(), check_residuals(), check_outliers(), and model_performance().

• item_alpha() was added as an alias for cronbachs_alpha().

• Further functions get a display(), print_md() and print_html() method.

Bug fixes

• Fixed issue in check_predictions() for binomial models with a response defined as proportion or matrix of successes and trials.

• print_md() for objects returned by check_itemscale() now include the footer with information about Cronbach's alpha and mean inter-item correlation.

Scientific Software - Peer-reviewed - R
Published by strengejacke 6 months ago

performance - performance 0.14.0

Breaking Changes

• The "Increased SE" column in the output of check_collinearity() was renamed into "adj. VIF" (=adjusted VIF). Furthermore, the computation of the adjusted VIF now correctly accounts for the numbers of levels (i.e. degrees of freedom) for factors.

New functions

• New function check_group_variation() to check within-/between-group variability (this function will replace check_heterogeneity_bias() in future releases.)

• New functions performance_reliability() and performance_dvour(). These functions provide information about the reliability of group-level estimates (i.e., random effects) in mixed models.

Changes

• Singularity checks with check_singularity() are now more efficient and also include the random effects for the dispersion component (from package glmmTMB). Furthermore, a check argument allows to check for general singularity (for the full model), or can return singularity checks for each random effects term separately.

Bug fixes

• Fixed issue with wrong computation of pseudo-R2 for some models where the base-model (null model) was updated using the original data, which could include missing values. Now the model frame is used, ensuring the correct number of observations in the returned base-model, thus calculating the correct log-likelihood and returning the correct pseudo-R2.

• Fixed examples in check_outliers().

Scientific Software - Peer-reviewed - R
Published by strengejacke 8 months ago

performance - performance 0.13.0

Breaking changes

• check_outliers() with method = "optics" now returns a further refined cluster selection, by passing the optics_xi argument to dbscan::extractXi().

• Deprecated arguments and alias-function-names have been removed.

• Argument names in check_model() that refer to plot-aesthetics (like dot_size) are now harmonized across easystats packages, meaning that these have been renamed. They now follow the pattern aesthetic_type, e.g. size_dot (instead of dot_size).

Changes

• Increased accuracy for check_convergence() for glmmTMB models.

• r2() and r2_mcfadden() now support beta-binomial (non-mixed) models from package glmmTMB.

• An as.numeric() resp. as.double() method for objects of class performance_roc was added.

• Improved documentation for performance_roc().

Bug fixes

• check_outliers() did not warn that no numeric variables were found when only the response variable was numeric, but all relevant predictors were not.

• check_collinearity() did not work for glmmTMB models when zero-inflation component was set to ~0.

Scientific Software - Peer-reviewed - R
Published by strengejacke 12 months ago

performance - performance 0.12.4

Changes

• check_dag() now also checks for colliders, and suggests removing it in the printed output.

• Minor revisions to the printed output of check_dag().

Bug fixes

• Fixed failing tests that broke due to changes in latest glmmTMB update.

Scientific Software - Peer-reviewed - R
Published by strengejacke about 1 year ago

performance - performance 0.12.3

New functions

• check_dag(), to check DAGs for correct adjustment sets.

Changes

• check_heterogeneity_bias() gets a nested argument. Furthermore, by can specify more than one variable, meaning that nested or cross-classified model designs can also be tested for heterogeneity bias.

Scientific Software - Peer-reviewed - R
Published by strengejacke over 1 year ago

performance - performance 0.12.2

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

Scientific Software - Peer-reviewed - R
Published by strengejacke over 1 year ago

performance - performance 0.12.1

General

• 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.

Bug fixes

• 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.

Scientific Software - Peer-reviewed - R
Published by strengejacke over 1 year ago

performance - performance 0.12.0

Breaking

• 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.

General

• 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().

Bug fixes

• 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.

Scientific Software - Peer-reviewed - R
Published by strengejacke over 1 year ago

performance - performance 0.11.0

New supported models

• Rudimentary support for models of class serp from package serp.

New functions

• 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()

General

• Improved error messages for check_model() when QQ-plots cannot be created.

• check_distribution() is more stable for possibly sparse data.

Bug fixes

• 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.

Scientific Software - Peer-reviewed - R
Published by strengejacke almost 2 years ago

performance - performance 0.10.9

Changes

• 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_mnodel()).

• 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.

Bug fixes

• 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.

Scientific Software - Peer-reviewed - R
Published by strengejacke almost 2 years ago

performance - performance 0.10.8

Changes

• 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).

Bug fixes

• Fixed CRAN check errors.

• Fixed issue with binned_residuals() for models with binomial family, where the outcome was a proportion.

Scientific Software - Peer-reviewed - R
Published by strengejacke about 2 years ago

performance - performance 0.10.7

Breaking changes

• 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.

Changes to functions

• 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.

Scientific Software - Peer-reviewed - R
Published by strengejacke about 2 years ago

performance - performance 0.10.6

General

• Support for nestedLogit models.

Changes to functions

• 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).

Bug fixes

• Fixed issues is check_model() for models that used data sets with variables of class "haven_labelled".

Scientific Software - Peer-reviewed - R
Published by rempsyc about 2 years ago

performance - performance 0.10.5

Changes to functions

• 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.

Bug fixes

• 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.

Scientific Software - Peer-reviewed - R
Published by strengejacke over 2 years ago

performance - performance 0.10.4

• 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).

Bug fixes

• Fixed issue in check_collinearity() for fixest models that used i() to create interactions in formulas.

Scientific Software - Peer-reviewed - R
Published by strengejacke over 2 years ago

performance - performance 0.10.3

New functions

• item_discrimination(), to calculate the discrimination of a scale's items.

Support for new models

• 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.

Changes to functions

• 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.

Bug fixes

• Fixed issue in print() method for check_collinearity(), which could mix up the correct order of parameters.

Scientific Software - Peer-reviewed - R
Published by strengejacke over 2 years ago

performance - performance 0.10.2

General

• Revised usage of insight::get_data() to meet forthcoming changes in the insight package.

Changes to functions

• check_collinearity() now accepts NULL for the ci argument.

Bug fixes

• 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.

Scientific Software - Peer-reviewed - R
Published by strengejacke almost 3 years ago

performance - performance 0.10.1

General

• Minor improvements to the documentation.

Changes to functions

• 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.

• check_predictions() accepts a bw argument (smoothing bandwidth), which is passed down to the plot() methods density-estimation. The default for the smoothing bandwidth bw has changed from "nrd0" to "nrd", which seems to produce better fitting plots for non-gaussian models.

• 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.

Bug fixes

• 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.

Scientific Software - Peer-reviewed - R
Published by strengejacke about 3 years ago

performance - performance 0.10.0

Breaking Change

• The minimum needed R version has been bumped to 3.6.

• The alias performance_lrt() was removed. Use test_lrt() resp. test_likelihoodratio().

New functions

• Following functions were moved from package parameters to performance: check_sphericity_bartlett(), check_kmo(), check_factorstructure() and check_clusterstructure().

Changes to functions

• 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.

Bug Fixes

• 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().

Scientific Software - Peer-reviewed - R
Published by strengejacke about 3 years ago

performance - performance 0.9.2

Scientific Software - Peer-reviewed - R
Published by strengejacke over 3 years ago

performance - performance 0.7.1

Release for JOSS paper.

Scientific Software - Peer-reviewed - R
Published by strengejacke over 4 years ago

performance - performance 0.4.8

Scientific Software - Peer-reviewed - R
Published by strengejacke over 5 years ago

performance - performance 0.4.7

DOI

Scientific Software - Peer-reviewed - R
Published by strengejacke over 5 years ago

performance - performance 0.4.2

Scientific Software - Peer-reviewed - R
Published by strengejacke about 6 years ago

performance - performance 0.4.0

Scientific Software - Peer-reviewed - R
Published by strengejacke about 6 years ago