sjstats 0.17.2

General

• Revised help-files for r2() and icc(), also by adding more references.

New functions

• re_grp_var() to find group factors of random effects in mixed models.

Changes to functions

• 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.

Bug fixes

• 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).
• Fixed issue with weights-argument in grpmean() when variable name was passed as character vector.
• Fixed issue with r2() for glmmTMB models with ar1 random effects structure.

sjstats 0.17.1

New functions

• 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.

Changes to functions

• 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.

Bug fixes

• Fixed issue with model_family() and link_inverse() for models fitted with pscl::hurdle() or pscl::zeroinfl().
• Fixed issue with wrong title in grpmean() for grouped data frames, when grouping variable was an unlabelled factor.
• Fix issue with model_frame() for coxph-models with polynomial or spline-terms.
• Fix issue with mediation() for logical variables.

sjstats 0.17.0

General

• Reduce package dependencies.

New functions

• wtd_ttest() to compute a weighted t-test.
• wtd_mwu() to compute a weighted Mann-Whitney-U or Kruskal-Wallis test.

Changes to functions

• robust() was revised, getting more arguments to specify different types of covariance-matrix estimation, and handling these more flexible.
• Improved 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.
• The statistics-argument in xtab_statistics() gets a "fisher"-option, to force Fisher's Exact Test to be used.
• Improved variance calculation in 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.
• To get consistent argument names accross functions, argument weight.by is now deprecated and renamed into weights.

Bug fixes

• Fix issues with effect size computation for repeated-measure Anova when using bootstrapping to compute confidence intervals.
• 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)).
• Fix issues in tidy_stan(), mcse(), hdi() and n_eff() for stan_polr()-models.
• Plotting equi_test() did not work for intercept-only models.

sjstats 0.16.0

General

• The S3-generics for functions like hdi(), rope(), equi_test() etc. are now more generic, and function usage for each supported object is now included in the documentation.
• Following functions are now S3-generic: icc(), r2(), p_value(), se(), and std_beta().
• Added print()-methods for some more functions, for a clearer output.
• Revised 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.
• Reduced package dependencies and removed apaTables and MBESS from suggested packages
• stanmvreg-models are now supported by many functions.

New 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.

Changes to functions

• 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.
• For Stan-models (brmsfit and stanreg), 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).

Bug fixes

• 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.
• Fix issue in 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.

sjstats 0.15.0

General

• Added new Vignette Statistics for Bayesian Models.

New functions

• 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.

Changes to functions

• 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.
• Functions that access model information (like 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.
• Effect sizes (anova_stats(), eta_sq() etc.) are now also computed for mixed models.
• To avoid confusion, n_eff() now computes the number of effective samples, and no longer its ratio in relation to the total number of samples.
• The column name for the ratio of the number of effective samples in tidy_stan() is now named neff_ratio, to avoid confusion.

Bug fixes

• Fixed issue in se() for icc()-objects, where random effect term could not be found.
• Fixed issue in se() for merMod-objects.
• Fixed issue in p_value() for mixed models with KR-approximation, which is now more accurate.

sjstats 0.10.0

New functions

• cv_error() and cv_compare() to compute the root mean squared error for test and training data from cross-validation.
• props() to calculate proportions in a vector, supporting multiple logical statements.
• or_to_rr() to convert odds ratio estimates into risk ratio estimates.
• mn(), md() and sm() to calculate mean, median or sum of a vector, but using na.rm = TRUE as default.
• S3-generics for svyglm.nb-models: family(), print(), formula(), model.frame() and predict().

Bug fixes

• Fixed error in computation of mse().