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Fixes issue in compare_parameters() for models from package blme.
Fixed conflict in model_parameters() when both include_reference = TRUE and pretty_names = "labels" were used. Now, pretty labels are correctly updated and preserved.
serp (serp).include_reference can now directly be set to TRUE in model_parameters() and doesn’t require a call to print() anymore.
compare_parameters() gains a include_reference argument, to add the reference category of categorical predictors to the parameters table.
print_md() for compare_parameters() now by default uses the tinytable package to create markdown tables. This allows better control for column heading spanning over multiple columns.
Fixed issue with parameter names for model_parameters() and objects from package epiR.
Fixed issue with exponentiate = TRUE for model_parameters() with models of class clmm (package ordinal), when model had no component column (e.g., no scale or location parameters were returned).
include_reference now also works when factor were created “on-the-fly” inside the model formula (i.e. y ~ as.factor(x)).
exponentiate argument of model_parameters() for marginaleffects::predictions() now defaults to FALSE, in line with all the other model_parameters() methods.model_parameters() for models of package survey now gives informative messages when bootstrap = TRUE (which is currently not supported).
n_factors() now also returns the explained variance for the number of factors as attributes.
model_parameters() for objects of package metafor now warns when unsupported arguments (like vcov) are used.
Improved documentation for pool_parameters().
print(include_reference = TRUE) for model_parameters() did not work when run inside a pipe-chain.
Fixed issues with format() for objects returned by compare_parameters() that included mixed models.
principal_components() and factor_analysis() now also work when argument n = 1.
print_md() for compare_parameters() now gains more arguments, similar to the print() method.
bootstrap_parameters() and model_parameters() now accept bootstrapped samples returned by bootstrap_model().
The print() method for model_parameters() now also yields a warning for models with logit-links when possible issues with (quasi) complete separation occur.
Fixed issue in print_html() for objects from package ggeffects.
Fixed issues for nnet::multinom() with wide-format response variables (using cbind()).
Minor fixes for print_html() method for model_parameters().
Robust standard errors (argument vcov) now works for plm models.
Minor improvements to factor analysis functions.
The ci_digits argument of the print() method for model_parameters() now defaults to the same value of digits.
model_parameters() for objects from package marginaleffects now also accepts the exponentiate argument.
The print(), print_html(), print_md() and format() methods for model_parameters() get an include_reference argument, to add the reference category of categorical predictors to the parameters table.
Fixed issue with wrong calculation of test-statistic and p-values in model_parameters() for fixest models.
Fixed issue with wrong column header for glm models with family = binomial("identiy").
Minor fixes for dominance_analysis().
nestedLogit (nestedLogit).model_parameters() now also prints correct “pretty names” when predictors where converted to ordered factors inside formulas, e.g. y ~ as.ordered(x).
model_parameters() now prints a message when the vcov argument is provided and ci_method is explicitly set to "profile". Else, when vcov is not NULL and ci_method is NULL, it defaults to "wald", to return confidence intervals based on robust standard errors.
lme.mipo for models with ordinal or categorical outcome.Added support for models of class hglm (hglm), mblogit (mclogit), fixest_multi (fixest), and phylolm / phyloglm (phylolm).
as.data.frame methods for extracting posterior draws via bootstrap_model() have been retired. Instead, directly using bootstrap_model() is recommended.
equivalence_test() gets a method for ggeffects objects from package ggeffects.
equivalence_test() now prints the SGPV column instead of % in ROPE. This is because the former % in ROPE actually was equivalent to the second generation p-value (SGPV) and refers to the proportion of the range of the confidence interval that is covered by the ROPE. However, % in ROPE did not refer to the probability mass of the underlying distribution of a confidence interval that was covered by the ROPE, hence the old column name was a bit misleading.
Fixed issue in model_parameters.ggeffects() to address forthcoming changes in the ggeffects package.
When an invalid or not supported value for the p_adjust argument in model_parameters() is provided, the valid options were not shown in correct capital letters, where appropriate.
Fixed bug in cluster_analysis() for include_factors = TRUE.
Fixed warning in model_parameters() and ci() for models from package glmmTMB when ci_method was either "profile" or "uniroot".
Reduce unnecessary warnings.
The deprecated argument df_method in model_parameters()was removed.
Output from model_parameters() for objects returned by manova() and car::Manova() is now more consistent.
Fixed issues in tests for mmrm models.
Fixed issue in bootstrap_model() for models of class glmmTMB with dispersion parameters.
Fixed failing examples.
flic and flac (logistf), mmrm (mmrm).model_parameters() now includes a Group column for stanreg or brmsfit models with random effects.
The print() method for model_parameters() now uses the same pattern to print random effect variances for Bayesian models as for frequentist models.
Fixed issue with the print() method for compare_parameters(), which duplicated random effects parameters rows in some edge cases.
Fixed issue with the print() method for compare_parameters(), which didn’t work properly when ci=NULL.
The deprecated argument df_method in model_parameters() is now defunct and throws an error when used.
The deprecated functions ci_robust(), p_robust() and standard_error_robust have been removed. These were superseded by the vcov argument in ci(), p_value(), and standard_error(), respectively.
The style argument in compare_parameters() was renamed into select.
p_function(), to print and plot p-values and compatibility (confidence) intervals for statistical models, at different levels. This allows to see which estimates are most compatible with the model at various compatibility levels.
p_calibrate(), to compute calibrated p-values.
model_parameters() and compare_parameters() now use the unicode character for the multiplication-sign as interaction mark (i.e. \u00d7). Use options(parameters_interaction = <value>) or the argument interaction_mark to use a different character as interaction mark.
The select argument in compare_parameters(), which is used to control the table column elements, now supports an experimental glue-like syntax. See this vignette Printing Model Parameters. Furthermore, the select argument can also be used in the print() method for model_parameters().
print_html() gets a font_size and line_padding argument to tweak the appearance of HTML tables. Furthermore, arguments select and column_labels are new, to customize the column layout of tables. See examples in ?display.
Consolidation of vignettes on standardization of model parameters.
Minor speed improvements.
model_parameters().BFBayesFactor no longer drops the BF column if the Bayes factor is NA.
The print() and display() methods for model_parameters() from Bayesian models now pass the ... to insight::format_table(), allowing extra arguments to be recognized.
Fixed footer message regarding the approximation method for CU and p-values for mixed models.
Fixed issues in the print() method for compare_parameters() with mixed models, when some models contained within-between components (see wb_component) and others did not.
Arguments that calculate effectsize in model_parameters() for htest, Anova objects and objects of class BFBayesFactor were revised. Instead of single arguments for the different effectsizes, there is now one argument, effectsize_type. The reason behind this change is that meanwhile many new type of effectsizes have been added to the effectsize package, and the generic argument allows to make use of those effect sizes.
The attribute name in PCA / EFA has been changed from data_set to dataset.
The minimum needed R version has been bumped to 3.6.
Removed deprecated argument parameters from model_parameters().
standard_error_robust(), ci_robust() and p_value_robust() are now deprecated and superseded by the vcov and vcov_args arguments in the related methods standard_error(), ci() and p_value(), respectively.
Following functions were moved from package parameters to performance: check_sphericity_bartlett(), check_kmo(), check_factorstructure() and check_clusterstructure().
Added sparse option to principal_components() for sparse PCA.
The pretty_names argument from the print() method can now also be "labels", which will then use variable and value labels (if data is labelled) as pretty names. If no labels were found, default pretty names are used.
bootstrap_model() for models of class glmmTMB and merMod gains a cluster argument to specify optional clusters when the parallel option is set to "snow".
P-value adjustment (argument p_adjust in model_parameters()) is now performed after potential parameters were removed (using keep or drop), so adjusted p-values is only applied to the parameters of interest.
Robust standard errors are now supported for fixest models with the vcov argument.
print() for model_parameters() gains a footer argument, which can be used to suppress the footer in the output. Further more, if footer = "" or footer = FALSE in print_md(), no footer is printed.
simulate_model() and simulate_parameters() now pass ... to insight::get_varcov(), to allow simulated draws to be based on heteroscedasticity consistent variance covariance matrices.
The print() method for compare_parameters() was improved for models with multiple components (e.g., mixed models with fixed and random effects, or models with count- and zero-inflation parts). For these models, compare_parameters(effects = "all", component = "all") prints more nicely.
dominance_analysis(), to compute dominance analysis statistics and designations.ci_random in model_parameters() defaults to NULL. It uses a heuristic to determine if random effects confidence intervals are likely to take a long time to compute, and automatically includes or excludes those confidence intervals. Set ci_random to TRUE or FALSE to explicitly calculate or omit confidence intervals for random effects.Fix issues in pool_parameters() for certain models with special components (like MASS::polr()), that failed when argument component was set to "conditional" (the default).
Fix issues in model_parameters() for multiple imputation models from package Hmisc.
It is now possible to hide messages about CI method below tables by specifying options("parameters_cimethod" = FALSE) (#722). By default, these messages are displayed.
model_parameters() now supports objects from package marginaleffects and objects returned by car::linearHypothesis().
Added predict() method to cluster_meta objects.
Reorganization of docs for model_parameters().
model_parameters() now also includes standard errors and confidence intervals for slope-slope-correlations of random effects variances.
model_parameters() for mixed models gains a ci_random argument, to toggle whether confidence intervals for random effects parameters should also be computed. Set to FALSE if calculation of confidence intervals for random effects parameters takes too long.
ci() for glmmTMB models with method = "profile" is now more robust.
Fixed issue with glmmTMB models when calculating confidence intervals for random effects failed due to singular fits.
display() now correctly includes custom text and additional information in the footer (#722).
Fixed issue with argument column_names in compare_parameters() when strings contained characters that needed to be escaped for regular expressions.
Fixed issues with unknown arguments in model_parameters() for lavaan models when standardize = TRUE.
model_parameters() now no longer treats data frame inputs as posterior samples. Rather, for data frames, now NULL is returned. If you want to treat a data frame as posterior samples, set the new argument as_draws = TRUE.sort_parameters() to sort model parameters by coefficient values.
standardize_parameters(), standardize_info() and standardise_posteriors() to standardize model parameters.
model_parameters()model_parameters() for mixed models from package lme4 now also reports confidence intervals for random effect variances by default. Formerly, CIs were only included when ci_method was "profile" or "boot". The merDeriv package is required for this feature.
model_parameters() for htest objects now also supports models from var.test().
Improved support for anova.rms models in model_parameters().
model_parameters() now supports draws objects from package posterior and deltaMethods objects from package car.
model_parameters() now checks arguments and informs the user if specific given arguments are not supported for that model class (e.g., "vcov" is currently not supported for models of class glmmTMB).
The vcov argument, used for computing robust standard errors, did not calculate the correct p-values and confidence intervals for models of class lme.
pool_parameters() did not save all relevant model information as attributes.
model_parameters() for models from package glmmTMB did not work when exponentiate = TRUE and model contained a dispersion parameter that was different than sigma. Furthermore, exponentiating falsely exponentiated the dispersion parameter.
options("parameters_summary" = TRUE/FALSE), which sets the default value for the summary argument in model_parameters() for non-mixed models.options("parameters_mixed_summary" = TRUE/FALSE), which sets the default value for the summary argument in model_parameters() for mixed models.Minor improvements for print() methods.
vcov_estimation, vcov_type, and robust arguments are deprecated in these functions: model_parameters(), parameters(), standard_error(), p_value(), and ci(). They are replaced by the vcov and vcov_args arguments.standard_error_robust() and p_value_robust() functions are superseded by the vcov and vcov_args arguments of the standard_error() and p_value() functions.Fixed minor issues and edge cases in n_clusters() and related cluster functions.
Fixed issue in p_value() that returned wrong p-values for fixest::feols().
Improved speed performance for model_parameters(), in particular for glm’s and mixed models where random effect variances were calculated.
Added more options for printing model_parameters(). See also revised vignette: https://easystats.github.io/parameters/articles/model_parameters_print.html
model_parameters()model_parameters() for mixed models gains an include_sigma argument. If TRUE, adds the residual variance, computed from the random effects variances, as an attribute to the returned data frame. Including sigma was the default behaviour, but now defaults to FALSE and is only included when include_sigma = TRUE, because the calculation was very time consuming.
model_parameters() for merMod models now also computes CIs for the random SD parameters when ci_method="boot" (previously, this was only possible when ci_method was "profile").
model_parameters() for glmmTMB models now computes CIs for the random SD parameters. Note that these are based on a Wald-z-distribution.
Similar to model_parameters.htest(), the model_parameters.BFBayesFactor() method gains cohens_d and cramers_v arguments to control if you need to add frequentist effect size estimates to the returned summary data frame. Previously, this was done by default.
Column name for coefficients from emmeans objects are now more specific.
model_prameters() for MixMod objects (package GLMMadaptive) gains a robust argument, to compute robust standard errors.
Fixed bug with ci() for class merMod when method="boot".
Fixed issue with correct association of components for ordinal models of classes clm and clm2.
Fixed issues in random_parameters() and model_parameters() for mixed models without random intercept.
Confidence intervals for random parameters in model_parameters() failed for (some?) glmer models.
Fix issue with default ci_type in compare_parameters() for Bayesian models.
Following functions were moved to the new datawizard package and are now re-exported from parameters package:
center()
convert_data_to_numeric()
data_partition()
demean() (and its aliases degroup() and detrend())
kurtosis()
rescale_weights()
skewness()
smoothness()
Note that these functions will be removed in the next release of parameters package and they are currently being re-exported only as a convenience for the package developers. This release should provide them with time to make the necessary changes before this breaking change is implemented.
Following functions were moved to the performance package:
check_heterogeneity()
check_multimodal()
The handling to approximate the degrees of freedom in model_parameters(), ci() and p_value() was revised and should now be more consistent. Some bugs related to the previous computation of confidence intervals and p-values have been fixed. Now it is possible to change the method to approximate degrees of freedom for CIs and p-values using the ci_method, resp. method argument. This change has been documented in detail in ?model_parameters, and online here: https://easystats.github.io/parameters/reference/model_parameters.html
Minor changes to print() for glmmTMB with dispersion parameter.
Added vignette on printing options for model parameters.
model_parameters()The df_method argument in model_parameters() is deprecated. Please use ci_method now.
model_parameters() with standardize = "refit" now returns random effects from the standardized model.
model_parameters() and ci() for lmerMod models gain a "residuals" option for the ci_method (resp. method) argument, to explicitly calculate confidence intervals based on the residual degrees of freedom, when present.
model_parameters() supports following new objects: trimcibt, wmcpAKP, dep.effect (in WRS2 package), systemfit
model_parameters() gains a new argument table_wide for ANOVA tables. This can be helpful for users who may wish to report ANOVA table in wide format (i.e., with numerator and denominator degrees of freedom on the same row).
model_parameters() gains two new arguments, keep and drop. keep is the new names for the former parameters argument and can be used to filter parameters. While keep selects those parameters whose names match the regular expression pattern defined in keep, drop is the counterpart and excludes matching parameter names.
When model_parameters() is called with verbose = TRUE, and ci_method is not the default value, the printed output includes a message indicating which approximation-method for degrees of freedom was used.
model_parameters() for mixed models with ci_method = "profile computes (profiled) confidence intervals for both fixed and random effects. Thus, ci_method = "profile allows to add confidence intervals to the random effect variances.
model_parameters() should longer fail for supported model classes when robust standard errors are not available.
n_factors() the methods based on fit indices have been fixed and can be included separately (package = "fit"). Also added a n_max argument to crop the output.
compare_parameters() now also accepts a list of model objects.
describe_distribution() gets verbose argument to toggle warnings and messages.
format_parameters() removes dots and underscores from parameter names, to make these more “human readable”.
The experimental calculation of p-values in equivalence_test() was replaced by a proper calculation p-values. The argument p_value was removed and p-values are now always included.
Minor improvements to print(), print_html() and print_md().
The random effects returned by model_parameters() mistakenly displayed the residuals standard deviation as square-root of the residual SD.
Fixed issue with model_parameters() for brmsfit objects that model standard errors (i.e. for meta-analysis).
Fixed issue in model_parameters for lmerMod models that, by default, returned residual degrees of freedom in the statistic column, but confidence intervals were based on Inf degrees of freedom instead.
Fixed issue in ci_satterthwaite(), which used Inf degrees of freedom instead of the Satterthwaite approximation.
Fixed issue in model_parameters.mlm() when model contained interaction terms.
Fixed issue in model_parameters.rma() when model contained interaction terms.
Fixed sign error for model_parameters.htest() for objects created with t.test.formula() (issue #552)
Fixed issue when computing random effect variances in model_parameters() for mixed models with categorical random slopes.
check_sphericity() has been renamed into check_sphericity_bartlett().
Removed deprecated arguments.
model_parameters() for bootstrapped samples used in emmeans now treats the bootstrap samples as samples from posterior distributions (Bayesian models).
SemiParBIV (GJRM), selection (sampleSelection), htest from the survey package, pgmm (plm).summary() method for model_parameters(), which is a convenient shortcut for print(..., select = "minimal").model_parameters()model_parameters() gains a parameters argument, which takes a regular expression as string, to select specific parameters from the returned data frame.
print() for model_parameters() and compare_parameters() gains a groups argument, to group parameters in the output. Furthermore, groups can be used directly as argument in model_parameters() and compare_parameters() and will be passed to the print() method.
model_parameters() for ANOVAs now saves the type as attribute and prints this information as footer in the output as well.
model_parameters() for htest-objects now saves the alternative hypothesis as attribute and prints this information as footer in the output as well.
model_parameters() passes arguments type, parallel and n_cpus down to bootstrap_model() when bootstrap = TRUE.
bootstrap_models() for merMod and glmmTMB objects gains further arguments to set the type of bootstrapping and to allow parallel computing.
bootstrap_parameters() gains the ci_method type "bci", to compute bias-corrected and accelerated bootstrapped intervals.
ci() for svyglm gains a method argument.
Fixed issue in model_parameters() for emmGrid objects with Bayesian models.
Arguments digits, ci_digits and p_digits were ignored for print() and only worked when used in the call to model_parameters() directly.
print() method for model_parameters().blrm (rmsb), AKP, med1way, robtab (WRS2), epi.2by2 (epiR), mjoint (joineRML), mhurdle (mhurdle), sarlm (spatialreg), model_fit (tidymodels), BGGM (BGGM), mvord (mvord)model_parameters()model_parameters() for blavaan models is now fully treated as Bayesian model and thus relies on the functions from bayestestR (i.e. ROPE, Rhat or ESS are reported) .
The effects-argument from model_parameters() for mixed models was revised and now shows the random effects variances by default (same functionality as random_parameters(), but mimicking the behaviour from broom.mixed::tidy()). When the group_level argument is set to TRUE, the conditional modes (BLUPs) of the random effects are shown.
model_parameters() for mixed models now returns an Effects column even when there is just one type of “effects”, to mimic the behaviour from broom.mixed::tidy(). In conjunction with standardize_names() users can get the same column names as in tidy() for model_parameters() objects.
model_parameters() for t-tests now uses the group values as column names.
print() for model_parameters() gains a zap_small argument, to avoid scientific notation for very small numbers. Instead, zap_small forces to round to the specified number of digits.
To be internally consistent, the degrees of freedom column for lqm(m) and cgam(m) objects (with t-statistic) is called df_error.
model_parameters() gains a summary argument to add summary information about the model to printed outputs.
Minor improvements for models from quantreg.
model_parameters supports rank-biserial, rank epsilon-squared, and Kendall’s W as effect size measures for wilcox.test(), kruskal.test, and friedman.test, respectively.
describe_distribution() gets a quartiles argument to include 25th and 75th quartiles of a variable.Fixed issue with non-initialized argument style in display() for compare_parameters().
Make print() for compare_parameters() work with objects that have “simple” column names for confidence intervals with missing CI-level (i.e. when column is named "CI" instead of, say, "95% CI").
Fixed issue with p_adjust in model_parameters(), which did not work for adjustment-methods "BY" and "BH".
Fixed issue with show_sigma in print() for model_parameters().
Fixed issue in model_parameters() with incorrect order of degrees of freedom.
Roll-back R dependency to R >= 3.4.
Bootstrapped estimates (from bootstrap_model() or bootstrap_parameters()) can be passed to emmeans to obtain bootstrapped estimates, contrasts, simple slopes (etc) and their CIs.
model_parameters() and related functions to obtain standard errors, p-values, etc.model_parameters() now always returns the confidence level for as additional CI column.
The rule argument in equivalenct_test() defaults to "classic".
crr (cmprsk), leveneTest() (car), varest (vars), ergm (ergm), btergm (btergm), Rchoice (Rchoice), garch (tseries)compare_parameters() (and its alias compare_models()) to show / print parameters of multiple models in one table.Estimation of bootstrapped p-values has been re-written to be more accurate.
model_parameters() for mixed models gains an effects-argument, to return fixed, random or both fixed and random effects parameters.
Revised printing for model_parameters() for metafor models.
model_parameters() for metafor models now recognized confidence levels specified in the function call (via argument level).
Improved support for effect sizes in model_parameters() from anova objects.
Fixed edge case when formatting parameters from polynomial terms with many degrees.
Fixed issue with random sampling and dropped factor levels in bootstrap_model().
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