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 parameters::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.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 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.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.r2() and icc(), also by adding more references.re_grp_var() to find group factors of random effects in mixed models.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.