NEWS.md
Weighted significance tests have been re-designed. The functions weighted_ttest()
, weighted_mannwhitney()
and weighted_chisqtest()
are no longer available. These are now re-implemented in t_test()
, mann_whitney_test()
and chi_squared_test()
. If weights are required, the weights
argument can be used. Furthermore, new functions for significance testing were added: kruskal_wallis_test()
and wilcoxon_test()
.
means_by_group()
and mean_n()
were removed. The replacements are datawizard::means_by_group()
and datawizard::row_means()
(using the min_valid
argument).
weighted_median()
, weighted_sd()
and weighted_mean()
were removed. Their replacements are datawizard::weighted_median()
, datawizard::weighted_sd()
and datawizard::weighted_mean()
.
Package dependency was dramatically reduced. sjstats now requires much fewer and much more light-weight packages to work.
Some minor bugs were fixed.
sjstats is being re-structured, and many functions are re-implemented in new packages that are part of a new project called easystats.
Therefore, following functions are now defunct:
mediation()
, , please use bayestestR::mediation()
.eta_sq()
, please use effectsize::eta_squared()
.omega_sq()
, please use effectsize::omega_squared()
.epsilon_sq()
, please use effectsize::epsilon_squared()
.odds_to_rr()
, please use effectsize::oddsratio_to_riskratio()
.std_beta()
, please use effectsize::standardize_parameters()
.robust()
, please use parameters::standard_error_robust()
.scale_weights()
, , please use datawizard::rescale_weights()
.Improved printing for weighted_mannwhitney()
.
weighted_chisqtest()
can now be computed for given probabilities.
means_by_group()
now contains numeric values in the returned data frame. Value formatting is completely done insight the print-method.
Updated imports.
eta_sq()
) now internally call the related functions from the effectsize package.chisq_gof()
.anova_stats()
with incorrect effect sizes for certain Anova types (that included an intercept).sjstats is being re-structured, and many functions are re-implemented in new packages that are part of a new project called easystats.
Therefore, following functions are now deprecated:
cohens_f()
, please use effectsize::cohens_f()
.std_beta()
, please use effectsize::standardize_parameters()
.tidy_stan()
, please use parameters::model_parameters()
.scale_weights()
, please use parameters::rescale_weights()
.robust()
, please use parameters::standard_error_robust()
.wtd_*()
have been renamed to weighted_*()
.svy_md()
was renamed to survey_median()
.mannwhitney()
is an alias for mwu()
.means_by_group()
is an alias for grpmean()
.sjstats is being re-structured, and many functions are re-implemented in new packages that are part of a new project called easystats. The aim of easystats is to provide a unifying and consistent framework to tame, discipline and harness the scary R statistics and their pesky models.
Therefore, following functions are now deprecated:
p_value()
, please use parameters::p_value()
se()
, please use parameters::standard_error()
design_effect()
is an alias for deff()
.samplesize_mixed()
is an alias for smpsize_lmm()
.crosstable_statistics()
is an alias for xtab_statistics()
.svyglm.zip()
to fit zero-inflated Poisson models for survey-designs.epsilon_sq()
, to compute epsilon-squared effect-size.sjstats is being re-structured, and many functions are re-implemented in new packages that are part of a new project called easystats. The aim of easystats is to provide a unifying and consistent framework to tame, discipline and harness the scary R statistics and their pesky models.
Therefore, following functions are now deprecated:
link_inverse()
, please use insight::link_inverse()
model_family()
, please use insight::model_info()
model_frame()
, please use insight::get_data()
pred_vars()
, please use insight::find_predictors()
re_grp_var()
, please use insight::find_random()
grp_var()
, please use insight::find_random()
resp_val()
, please use insight::get_response()
resp_var()
, please use insight::find_response()
var_names()
, please use insight::clean_names()
overdisp()
, please use performance::check_overdispersion()
zero_count()
, please use performance::check_zeroinflation()
converge_ok()
, please use performance::check_convergence()
is_singular()
, please use performance::check_singularity()
reliab_test()
, please use performance::item_reliability()
split_half()
, please use performance::item_split_half()
predictive_accurarcy()
, please use performance::performance_accuracy()
cronb()
, please use performance::cronbachs_alpha()
difficulty()
, please use performance::item_difficulty()
mic()
, please use performance::item_intercor()
pca()
, please use parameters::principal_components()
pca_rotate()
, please use parameters::principal_components()
r2()
, please use performance::r2()
icc()
, please use performance::icc()
rmse()
, please use performance::rmse()
rse()
, please use performance::rse()
mse()
, please use performance::mse()
hdi()
, please use bayestestR::hdi()
cred_int()
, please use bayestestR::ci()
rope()
, please use bayestestR::rope()
n_eff()
, please use bayestestR::effective_sample()
equi_test()
, please use bayestestR::equivalence_test()
multicollin()
, please use performance::check_collinearity()
normality()
, please use performance::check_normality()
autocorrelation()
, please use performance::check_autocorrelation()
heteroskedastic()
, please use performance::check_heteroscedasticity()
outliers()
, please use performance::check_outliers()
eta_sq()
) get a method
-argument to define the method for computing confidence intervals from bootstrapping.smpsize_lmm()
could result in negative sample-size recommendations. This was fixed, and a warning is now shown indicating that the parameters for the power-calculation should be modified.r
in mwu()
if group-factor contained more than two groups.model_family()
, link_inverse()
or model_frame()
: MixMod
(package GLMMadaptive), MCMCglmm, mlogit
and gmnl
.cred_int()
, to compute uncertainty intervals of Bayesian models. Mimics the behaviour and style of hdi()
and is thus a convenient complement to functions like posterior_interval()
.equi_test()
now finds better defaults for models with binomial outcome (like logistic regression models).r2()
for mixed models now also should work properly for mixed models fitted with rstanarm.anova_stats()
and alike (e.g. eta_sq()
) now all preserve original term names.model_family()
now returns $is_count = TRUE
, when model is a count-model, and $is_beta = TRUE
for models with beta-family.pred_vars()
checks that return value has only unique values.pred_vars()
gets a zi
-argument to return the variables from a model’s zero-inflation-formula.wtd_sd()
and wtd_mean()
when weight was NULL
(which usually shoudln’t be the case anyway).deparse()
, cutting off very long formulas in various functions.dplyr::n()
, to meet forthcoming changes in dplyr 0.8.0.boot_ci()
gets a ci.lvl
-argument.rotation
-argument in pca_rotate()
now supports all rotations from psych::principal()
.pred_vars()
gets a fe.only
-argument to return only fixed effects terms from mixed models, and a disp
-argument to return the variables from a model’s dispersion-formula.icc()
for Bayesian models gets a adjusted
-argument, to calculate adjusted and conditional ICC (however, only for Gaussian models).icc()
for non-Gaussian Bayes-models, a message is printed that recommends setting argument ppd
to TRUE
.resp_val()
and resp_var()
now also work for brms-models with additional response information (like trial()
in formula).resp_var()
gets a combine
-argument, to return either the name of the matrix-column or the original variable names for matrix-columns.model_frame()
now also returns the original variables for matrix-column-variables.model_frame()
now also returns the variable from the dispersion-formula of glmmTMB-models.model_family()
and link_inverse()
now supports glmmPQL, felm and lm_robust-models.anova_stats()
and alike (omeqa_sq()
etc.) now support gam-models from package gam.p_value()
now supports objects of class svyolr
.omega_sq()
and eta_sq()
give more informative messages when using non-supported objects.r2()
and icc()
give more informative warnings and messages.tidy_stan()
supports printing simplex parameters of monotonic effects of brms models.grpmean()
and mwu()
get a file
and encoding
argument, to save the HTML output as file.model_frame()
now correctly names the offset-columns for terms provided as offset
-argument (i.e. for models where the offset was not specified inside the formula).weights
-argument in grpmean()
when variable name was passed as character vector.r2()
for glmmTMB models with ar1
random effects structure.wtd_chisqtest()
to compute a weighted Chi-squared test.wtd_median()
to compute the weighted median of variables.wtd_cor()
to compute weighted correlation coefficients of variables.mediation()
can now cope with models from different families, e.g. if the moderator or outcome is binary, while the treatment-effect is continuous.model_frame()
, link_inverse()
, pred_vars()
, resp_var()
, resp_val()
, r2()
and model_family()
now support clm2
-objects from package ordinal.anova_stats()
gives a more informative message for non-supported models or ANOVA-options.model_family()
and link_inverse()
for models fitted with pscl::hurdle()
or pscl::zeroinfl()
.grpmean()
for grouped data frames, when grouping variable was an unlabelled factor.model_frame()
for coxph-models with polynomial or spline-terms.mediation()
for logical variables.wtd_ttest()
to compute a weighted t-test.wtd_mwu()
to compute a weighted Mann-Whitney-U or Kruskal-Wallis test.robust()
was revised, getting more arguments to specify different types of covariance-matrix estimation, and handling these more flexible.print()
-method for tidy_stan()
for brmsfit-objects with categorical-families.se()
now also computes standard errors for relative frequencies (proportions) of a vector.r2()
now also computes r-squared values for glmmTMB-models from genpois
-families.r2()
gives more precise warnings for non-supported model-families.xtab_statistics()
gets a weights
-argument, to compute measures of association for contingency tables for weighted data.statistics
-argument in xtab_statistics()
gets a "fisher"
-option, to force Fisher’s Exact Test to be used.icc()
for generalized linear mixed models with Poisson or negative binomial families.icc()
gets an adjusted
-argument, to calculate the adjusted and conditional ICC for mixed models.weight.by
is now deprecated and renamed into weights
.grpmean()
now also adjusts the n
-columm for weighted data.icc()
, re_var()
and get_re_var()
now correctly compute the random-effect-variances for models with multiple random slopes per random effect term (e.g., (1 + rs1 + rs2 | grp)
).tidy_stan()
, mcse()
, hdi()
and n_eff()
for stan_polr()
-models.equi_test()
did not work for intercept-only models.