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.
These can then be passed to 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()
.
coxr
(coxrobust), coeftest
(lmtest), ivfixed
(ivfixed), ivprobit
(ivprobit), riskRegression
(riskRegression), fitdistr
(MASS),
yuen
, t1way
, onesampb
, mcp1
and mcp2
(WRS2), Anova.mlm
(car),
rqs
(quantreg), lmodel2
(lmodel2), summary.lm
, PMCMR
, osrt
and
trendPMCMR
(PMCMRplus), bamlss
(bamlss).print_html()
as an alias for display(format = "html")
. This allows to
print tabular outputs from data frames (as returned by most functions in
parameters) into nicely rendered HTML markdown tables.Added more effect size measures to model_parameters()
for htest
objects.
model_parameters()
for anova objects gains a power
argument, to calculate
the power for each parameter.
ci()
for models from lme4 and glmmTMB can now computed profiled
confidence intervals, using method = "profile"
. Consequently,
model_parameters()
with df_method = "profile"
also computes profiled
confidence intervals. For models of class glmmTMB
, option "uniroot"
is
also available.
model_parameters()
for t-tests when standardize_d = TRUE
, did not return
columns for the group-specific means.
Fixed issue in p_value()
for fixest::feols()
.
Fixed issue in model_parameters()
for glmer()
models with p-values that
were calculated with df_method = "ml1"
or df_method = "betwithin"
.
Fixed issue in model_parameters()
for multinomial models when response was a
character vector (and no factor).
Fixed issue in print_md()
for model-parameters objects from Bayesian
models.
Fixed issues with printing of model parameters for multivariate response models from brms.
Fixed issue with paired t-tests and model_parameters()
.
format_p_adjust()
, to create pretty names for p-adjustment methods.Fixed breaking code / failing tests due to latest effectsize update.
Fixed issue with model_parameters()
for models of class mlm
.
Undocumented arguments digits
, ci_digits
and p_digits
worked for
print()
, but not when directly called inside model_parameters()
. Now,
model_parameters(model, digits = 5, ci_digits = 8)
works again.
Fixed some minor printing-issues.
The default-method for effect sizes in model_parameters()
for Anova-models
(i.e. when arguments omega_squared
, eta_squared
or epsilon_squared
are
set to TRUE
) is now "partial"
, as initially intended.
Column names for degrees of freedom were revised. "df_residual"
was replaced
by the more generic "df_error"
. Moreover, models of class htest
now also
have the column name "df_error"
and no longer "df"
(where applicable).
Some re-exports for functions that were moved to insight longer ago, were now removed.
Glm
(rms), mediate
(mediation).
model_parameters()
supports Gam
models (gam), ridgelm
(MASS),
htest
objects from oneway.test()
, chisq.test()
, prop.test()
,
mcnemar.test()
and pairwise.htest
objects, mcmc.list
(e.g. from
bayesGARCH).
display()
, to format output from package-functions into different formats.
print_md()
as an alias for display(format = "markdown")
. This allows to
print tabular outputs from data frames (as returned by most functions in
parameters) into nicely rendered markdown tables.
format()
, to create a "pretty data frame" with nicer column names and
formatted values. This is one of the worker-functions behind print()
or
print_md()
.
model_parameters()
model_parameters()
for Anova-models (of class aov
, anova
etc.) gains a
ci
-argument, to add confidence intervals to effect size parameters.
model_parameters()
for htest
objects gains a cramers_v
and phi
argument, to compute effect size parameters for objects from chisq.test()
,
and a standardized_D
argument, to compute effect size parameters for objects
from t.test()
.
model_parameters()
for metafor
-models is more stable when called from
inside functions.
model_parameters()
for metaBMA-models now includes prior information for
the meta-parameters.
model_parameters()
for meta-analysis-models gains a
include_studies
-argument, to include or remove studies from the output.
model_parameters()
for gam-models now includes the residual df for smooth
terms, and no longer the reference df.
Slightly revised and improved the print()
method for model_parameters()
.
describe_distribution()
now includes the name of the centrality index in the
CI
-column, when centrality = "all"
.
pool_parameters()
gains a details
-argument. For mixed models, and if
details = TRUE
, random effect variances will also be pooled.
Fixed issue in ci()
for lme models with non-positive definite
variance-covariance.
Fixed issue in model_parameters()
for nnet::multinom()
, lqmm::lqm()
,
mgcv::gam()
, and margins::margins()
models, and models from package
blme.
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