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check_dag() now also checks for colliders, and
suggests removing it in the printed output.
Minor revisions to the printed output of
check_dag().
check_dag(), to check DAGs for correct adjustment
sets.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.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.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.
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.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).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).
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.
print() method for
check_collinearity(), which could mix up the correct order
of parameters.insight::get_data() to meet
forthcoming changes in the insight package.check_collinearity() now accepts NULL for
the ci argument.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.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().
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.model_performance() output
for kmeans objects (#453)icc() is now named
“unadjusted” ICC.performance_cv() for cross-validated model
performance.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.
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()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.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.
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.
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.
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().
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.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.