NEWS.md
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()
std_beta()
, please use parameters::parameters_standardize()
design_effect()
is an alias for deff()
.samplesize_mixed()
is an alias for smpsize_lmm()
.svyglm.zip()
to fit zero-inflated Poisson models for survey-designs.phi()
and cramer()
can now compute confidence intervals.tidy_stan()
removes prior parameters from output.tidy_stan()
now also prints the probability of direction.odds_to_rr()
.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 performance::principal_components()
pca_rotate()
, please use performance::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
.se()
and get_re_var()
for objects returned by icc()
.icc()
for Stan-models.var_names()
did not clear terms with log-log transformation, e.g. log(log(y))
.model_frame()
for models with splines with only one column.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.hdi()
, rope()
, equi_test()
etc. are now more generic, and function usage for each supported object is now included in the documentation.icc()
, r2()
, p_value()
, se()
, and std_beta()
.print()
-methods for some more functions, for a clearer output.r2()
for mixed models (packages lme4, glmmTMB). The r-squared value should be much more precise now, and reports the marginal and conditional r-squared values.stanmvreg
-models are now supported by many functions.binned_resid()
to plot binned residuals for logistic regression models.error_rate()
to compute model quality for logistic regression models.auto_prior()
to quickly create automatically adjusted priors for brms-models.difficulty()
to compute the item difficulty.icc()
gets a ppd
-argument for Stan-models (brmsfit and stanreg), which performs a variance decomposition based on the posterior predictive distribution. This is the recommended way for non-Gaussian models.icc()
now also computes the HDI for the ICC and random-effect variances. Use the prob
-argument to specify the limits of this interval.link_inverse()
and model_family()
now support clmm-models (package ordinal) and glmRob and lmRob-models (package robust).model_family()
gets a multi.resp
-argument, to return a list of family-informations for multivariate-response models (of class brmsfit
or stanmvreg
).link_inverse()
gets a multi.resp
-argument, to return a list of link-inverse-functions for multivariate-response models (of class brmsfit
or stanmvreg
).p_value()
now supports rlm-models (package MASS).check_assumptions()
for single models with as.logical = FALSE
now has a nice print-method.eta_sq()
and omega_sq()
now also work for repeated-measure Anovas, i.e. Anova with error term (requires broom > 0.4.5).model_frame()
and var_names()
now correctly cleans nested patterns like offset(log(x + 10))
from column names.model_frame()
now returns proper column names from gamm4 models.model_frame()
did not work when the model frame had spline-terms and weights.robust()
when exponentiate = TRUE
and conf.int = FALSE
.reliab_test()
returned an error when the provided data frame has less than three columns, instead of returning NULL
.equi_test()
to test if parameter values in Bayesian estimation should be accepted or rejected.mediation()
to print a summary of a mediation analysis from multivariate response models fitted with brms.link_inverse()
now also returns the link-inverse function for cumulative-family brms-models.model_family()
now also returns an is_ordinal
-element with information if the model is ordinal resp. a cumulative link model.model_family()
) now better support vglm
-models (package VGAM).r2()
now also calculates the standard error for brms or stanreg models.r2()
gets a loo
-argument to calculate LOO-adjusted rsquared values for brms or stanreg models. This measure comes conceptionally closer to an adjusted r-squared measure.anova_stats()
, eta_sq()
etc.) are now also computed for mixed models.n_eff()
now computes the number of effective samples, and no longer its ratio in relation to the total number of samples.tidy_stan()
is now named neff_ratio, to avoid confusion.