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performance 0.3.1

Deprecated and Defunct

  • principal_components() is now deprecated and re-implemented in the parameters-package. Please use parameters::principal_components() now.

Changes to functions

  • check_outliers() now also works on data frames.
  • performance_score() now also works on stan_lmer() and stan_glmer() objects.

Bug fixes

  • Fixed bug in compare_performance() that toggled a warning although models were fit from same data.
  • Fixed bug in principal_components() that did not return more components when rotation was not Varimax.

performance 0.3.0

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-inlfated 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.

performance 0.2.0

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.

New performance-functions

  • 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).