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().