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"
.
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_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.
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.