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.

performance 0.12.2

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

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.

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.

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.

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.

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.

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.

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".

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.