Used more accurate analytic approach to calculate normal distributions for
the SGPV in equivalence_test() and used in p_significance().
Added p_direction() methods for frequentist models. This is a convenient
way to test the direction of the effect, which formerly was already (and still
is) possible with pd = TRUE in model_parameters().
glm_weightit, multinom_weightit and ordinal_weightit
from package WeightIt.Added p_significance() methods for frequentist models.
Methods for degrees_of_freedom() have been removed. degrees_of_freedom()
now calls insight::get_df().
model_parameters() for data frames and draws objects from package
posterior also gets an exponentiate argument.
equivalence_test(),
which should now be more accurate related to the proportion of the interval
that falls inside the ROPE. Formerly, the confidence interval was simply treated
as uniformly distributed when calculating the SGPV, now the interval is assumed
to be normally distributed.svy2lme models from package svylme.standardize_parameters() now also prettifies labels of factors.Fixed issue with equivalence_test() when ROPE range was not symmetrically
centered around zero (e.g., range = c(-99, 0.1)).
model_parameters() for anova() from mixed models now also includes the
denominator degrees of freedom in the output (df_error).
print(..., pretty_names = "labels") for tobit-models from package AER now
include value labels, if available.
Patch release, to ensure that performance runs with older version of datawizard on Mac OS X with R (old-release).
Deprecated arguments in model_parameters() for htest, aov and
BFBayesFactor objects were removed.
Argument effectsize_type is deprecated. Please use es_type now. This change
was necessary to avoid conflicts with partial matching of argument names (here:
effects).
Support for objects from stats::Box.test().
Support for glmgee models from package glmtoolbox.
Fixed edge case in predict() for factor_analysis().
Fixed wrong ORCID in DESCRIPTION.
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.
Added options to set defaults for different arguments. Currently supported:
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.
Robust uncertainty estimates:
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().