NEWS.md
alternative = "greater"
). (To restore previous behavior, set ci = .9, alternative = "two.sided"
.)adjust()
, change_scale()
, normalize()
, ranktransform()
, standardize()
(data), and unstandardize()
have moved to the new {datawizard}
package!oddsratio_to_riskratio()
can now convert OR coefficients to RR coefficients from a logistic GLM.alternative
argument which can be used to make one- or two-sided CIs.interpret()
now accepts as input the results from cohens_d()
, eta_squared()
, rank_biserial()
, etc.interpret_pd()
for the interpretation of the Probability of Direction.kendalls_w()
CIs now correctly bootstrap samples from the raw data (previously the rank-transformed data was sampled from).cohens_d()
, sd_pooled()
and rank_biserial()
now properly respect when y
is a grouping character vector.effectsize()
for Chi-squared test of goodness-of-fit now correctly respects non-uniform expected probabilities ( #352 ).interpret_bf()
now accepts log(BF)
as input.eta_squared()
family now indicate the type of sum-of-squares used.rank_biserial()
estimates CIs using the normal approximation (previously used bootstrapping).hedges_g()
now used exact bias correction (thanks to @mdelacre for the suggestion!)glass_delta()
now estimates CIs using the NCP method based on Algina et al (2006).eta_squared()
family returns correctly returns the type 2/3 effect sizes for mixed ANOVAs fit with afex
.cohens_d()
family now correctly deals with missing factor levels ( #318 )cohens_d()
/ hedges_g()
minor fix for CI with unequal variances.mad_pooled()
(the robust version of sd_pooled()
) now correctly pools the the two samples.standardize_parameters()
+ eta_sqaured()
support tidymodels
(when that the underlying model is supported; #311 ).cohens_d()
family now supports Pairs()
objects as input.standardize_parameters()
gains the include_response
argument (default to TRUE
) ( #309 ).kendalls_w()
now actually returns correct effect size. Previous estimates were incorrect, and based on transposing the groups and blocks.effectsize
now supports R >= 3.4
.
standardize_parameters()
now supports bootstrapped estimates (from parameters::bootstrap_model()
and parameters::bootstrap_parameters()
).unstandardize()
which will reverse the effects of standardize()
.interpret_kendalls_w()
to interpret Kendall’s coefficient of concordance.eta_squared()
family of functions can now also return effect sizes for the intercept by setting include_intercept = TRUE
( #156 ).standardize()
can now deal with dates ( #300 ).oddsratio()
and riskratio()
- order of groups has been changed (the first groups is now the treatment group, and the second group is the control group), so that effect sizes are given as treatment over control (treatment / control) (previously was reversed). This is done to be consistent with other functions in R and in effectsize
.cohens_h()
effect size for comparing two independent proportions.
rank_biserial()
, cliffs_delta()
, rank_epsilon_squared()
and kendalls_w()
functions for effect sizes for rank-based tests.
adjust()
gains keep_intercept
argument to keep the intercept.
eta_squared()
family of functions supports Anova.mlm
objects (from the car
package).
supports Cohen’s g for McNemar’s test.
Extracts OR from Fisher’s Exact Test in the 2x2 case.
eta2_to_f2()
/ f2_to_eta2()
to convert between two types of effect sizes for ANOVA ( #240 ).
cohens_d()
family of functions gain mu
argument.
adjust()
properly works when multilevel = TRUE
.
cohens_d()
family / sd_pooled()
now properly fails when given a missing column name.
effectsize()
for htest
objects now tries first to extract the data used for testing, and computed the effect size directly on that data.
cohens_d()
family / sd_pooled()
now respect any transformations (e.g. I(log(x) - 3) ~ factor(y)
) in a passed formula.
eta_squared()
family of functions gains a verbose
argument.
verbose
argument more strictly respected.
glass_delta()
returns CIs based on the bootstrap.
cohens_d()
and glass_delta()
: The correction
argument has been deprecated, in favor of it being correctly implemented in hedges_g()
( #222 ).
eta_squared_posterior()
no longer uses car::Anova()
by default.
effectsize()
gains type =
argument for specifying which effect size to return.
eta_squared_posterior()
can return a generalized Eta squared.
oddsratio()
and riskratio()
functions for 2-by-2 contingency tables.
standardize()
gains support for mediation::mediate()
models.
eta_squared()
family available for manova
objects.
eta_squared()
family of functions returns non-partial effect size for one-way between subjects design (#180).hedges_g()
correctly implements the available bias correction methods ( #222 ).
Fixed width of CI for Cohen’s d and Hedges’ g when using non-pooled SD.
standardize_parameters()
for multi-component models (such as zero-inflated) now returns the unstandardized parameters in some cases where standardization is not possible (previously returned NA
s).
Column name changes:
eta_squared()
/ F_to_eta2
families of function now has the Eta2
format, where previously was Eta_Sq
.
cramers_v
is now Cramers_v
effectsize()
added support for BayesFactor
objects (Cohen’s d, Cramer’s v, and r).
cohens_g()
effect size for paired contingency tables.
Generalized Eta Squared now available via eta_squared(generalized = ...)
.
eta_squared()
, omega_squared()
and epsilon_squared()
fully support aovlist
, afex_aov
and mlm
(or maov
) objects.
standardize_parameters()
can now return Odds ratios / IRRs (or any exponentiated parameter) by setting exponentiate = TRUE
.
Added cohens_f_squared()
and F_to_f2()
for Cohen’s f-squared.
cohens_f()
/ cohens_f_squared()
can be used to estimate Cohen’s f for the R-squared change between two models.
standardize()
and standardize_info()
work with weighted models / data ( #82 ).
Added hardlyworking
(simulated) dataset, for use in examples.
interpret_*
( #131 ):
interpret_omega_squared()
added "cohen1992"
rule.
interpret_p()
added Redefine statistical significance rules.
oddsratio_to_riskratio()
for converting OR to RR.
CIs for Omega-/Epsilon-squared and Adjusted Phi/Cramer’s V return 0s instead of negative values.
standardize()
for data frames gains the remove_na
argument for dealing with NA
s ( #147 ).
standardize()
and standardize_info()
now (and by extension, standardize_parameters()
) respect the weights in weighted models when standardizing ( #82 ).
Internal changes to standardize_parameters()
(reducing co-dependency with parameters
) - argument parameters
has been dropped.
ranktransform(sign = TURE)
correctly (doesn’t) deal with zeros.
effectsize()
for htest
works with Spearman and Kendall correlations ( #165 ).
cramers_v()
and phi()
now work with goodness-of-fit data ( #158 )
standardize_parameters()
for post-hoc correctly standardizes transformed outcome.
Setting two_sd = TRUE
in standardize()
and standardize_parameters()
(correctly) on uses 2-SDs of the predictors (and not the response).
standardize_info()
/ standardize_parameters(method = "posthoc")
work for zero-inflated models ( #135 )
standardize_info(include_pseudo = TRUE)
/ standardize_parameters(method = "pseudo")
are less sensitive in detecting between-group variation of within-group variables.
interpret_oddsratio()
correctly treats extremely small odds the same as treats extremely large ones.
standardize_parameters(method = "pseudo")
returns pseudo-standardized coefficients for (G)LMM models.
d_to_common_language()
for common language measures of standardized differences (a-la Cohen’s d).
r_to_odds()
family is now deprecated in favor of r_to_oddsratio()
.
interpret_odds()
is now deprecated in favor of interpret_oddsratio()
phi()
and cramers_v()
did not respect the CI argument ( #111 ).
standardize()
/ standardize_parameters()
properly deal with transformed data in the model formula ( #113 ).
odds_to_probs()
was mis-treating impossible odds (NEVER TELL ME THE ODDS! #123 )
eta_squared_posterior()
for estimating Eta Squared for Bayesian models.
eta_squared()
, omega_squared()
and epsilon_squared()
now works with
ols
/ rms
models.effectsize()
for class htest
supports oneway.test(...)
.
Fix minor miss-calculation of Chi-squared for 2*2 table with small samples ( #102 ).
Fixed miss-calculation of signed rank in ranktransform()
( #87 ).
Fixed bug in standardize()
for standard objects with non-standard class-attributes (like vectors of class haven_labelled
or vctrs_vctr
).
Fix effectsize()
for one sample t.test(...)
( #95 ; thanks to pull request by @mutlusun )
standardize_parameters()
now returns CIs ( #72 )
eta_squared()
, omega_squared()
and epsilon_squared()
now works with
gam
models.
afex
models.
lme
and anova.lme
objects.
New function equivalence_test()
for effect sizes.
New plotting methods in the see
package.
New general purpose effectsize()
function.
Effectsize for differences have CI methods, and return a data frame.
Effectsize for ANOVA all have CI methods, and none are based on bootstrapping.
New effect sizes for contingency tables (phi()
and cramers_v()
).
chisq_to_phi()
/ cramers_v()
functions now support CIs (via the ncp method), and return a data frame.
F_to_eta2()
family of functions now support CIs (via the ncp method), and return a data frame.
t_to_d()
and t_to_r()
now support CIs (via the ncp method), and return a data frame.
standardize()
for model-objects has a default-method, which usually accepts all models. Exception for model-objects that do not work will be added if missing.
standardize.data.frame()
gets append
and suffix
arguments, to add (instead of replace) standardized variables to the returned data frame.
eta_squared()
, omega_squared()
and epsilon_squared()
now works
output from parameters::model_parameters()
.
mlm
models.
Fix cohens_d()
’s dealing with formula input (#44).
sd_pooled()
now returns the… pooled sd (#44).
t_to_d()
, argument pooled
is now paired
.standardize.data.frame()
did not work when variables had missing values.
Fixed wrong computation in standardize()
when two_sd = TRUE
.
Fixed bug with missing column names in standardize_parameters()
for models with different components (like count and zero-inflation).
standardize_parameters()
and standardize()
now support models from packages brglm, brglm2, mixor, fixest, cgam, cplm, cglm, glmmadmb and complmrob.