sjstats: Statistical Functions for Regression Models.
Daniel Lüdecke · Zenodo (CERN European Organization for Nuclear Research) · 2018
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.