sjstats 0.15.0 2018-06-06

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 0.14.3 2018-05-02

General

  • Remove tidyverse from suggested packages, as requested by maintainers.

Breaking Changes

  • mwu() now requires a data frame as first argument, followed by the names of the two variables to perform the Mann-Whitney-U-Test on.

Changes to functions

  • tidy_stan() was improved especially for more complex multilevel models.
  • Make tidy_stan() for large brmsfit-objects (esp. with random effects) more efficient.
  • Better print()-method for tidy_stan(), hdi(), rope(), icc() and some other functions.
  • link_inverse() now also should return the link-inverse function for most (or some or all?) custom families of brms-models.
  • The weight.by-arguments in grpmean() and mwu() now should be a variable name from a variable in x, and no longer a separate vector.

New functions

  • model_family() to get model-information about family and link-functions. This function is intended to be “generic” and work with many different model objects, because not all packages provide a family() function.

Bug fixes

  • Fix issue with omega_sq(), eta_sq() etc. when confidence intervals were computed with bootstrapping and the model-formula contained function calls like scale() or as.factor().
  • Fix issue with p_value() for unconditional mixed models.
  • Fix typo in xtab_statistics().
  • Fix issue with wrong calculation of Nagelkerke’s r-squared value in r2().
  • Fix issue for factors with character leves in typical_value(), when argument fun for factors was set to mode.
  • Don’t show prior-samples in hdi(), tidy_stan() etc. for brmsfit-objects.
  • Fixed issues in model_frame()with spline-terms when missing values were removed due to casewise deletion.

sjstats 0.14.2 2018-03-25

General

  • Revise examples, vignettes and package description to make sure all used packages are available for CRAN checks on operating systems.

sjstats 0.14.2 2018-03-25

New functions

  • residuals.svyglm.nb() as S3-generic residuals() method for objects fitted with svyglm.nb().

Changes to functions

  • icc() gets a posterior-argument, to compute ICC-values from brmsfit-objects, for the whole posterior distribution.
  • icc() now gives a warning when computed for random-slope-intercept models, to warn user about probably inappropriate inference.
  • r2() now computes Bayesian version of R-squared for stanreg and brmsfit objects.
  • Argument prob in hdi() now accepts a vector of scalars to compute HDIs for multiple probability tresholds at once.
  • Argument probs in tidy_stan() was renamed into prob, to be consistent with hdi().
  • mwu() gets an out-argument, to print output to console, or as HTML table in the viewer or web browser.
  • scale_weights() now also works if weights have missing values.
  • hdi() and rope() get data.frame-methods.
  • omega_sq() and eta_sq() get a ci.lvl-argument to compute confidence intervals for the effect size statistics.
  • omega_sq(), eta_sq() and cohens_f() now always return a data frame with at least two columns: term name and effect size. Confidence intervals are added as additional columns, if the ci.lvl-argument is TRUE.
  • omega_sq() gets a partial-argument to compute partial omega-squared.
  • omega_sq(), eta_sq(), cohens_f() and anova_stats() now support anova.rms-objects from the rms-package.

Bug fixes

  • Fix unnecessary warning for tibbles in mic().
  • Make sure that model_frame() does not return duplicated column names.
  • Fix issue in tidy_stan() with incorrect n_eff statistics for sigma parameter in mixed models.
  • Fix issue in tidy_stan(), which did not work when probs was of length greater than 2.
  • Fix issue in icc() with brmsfit-models, which was broken probably due to internal changes in brms.

sjstats 0.14.1 2018-02-04

General

Changes to functions

  • var_names() now also cleans variable names from variables modeled with the mi() function (multiple imputation on the fly in brms).
  • reliab_test() gets an out-argument, to print output to console, or as HTML table in the viewer or web browser.

Bug fixes

  • Fix issues with mcse(), n_eff() and tidy_stan() with more complex brmsfit-models.
  • Fix issue in typical_value() to prevent error for R-oldrel-Windows.
  • model_frame() now returns response values from models, which are in matrix form (bound with cbind()), as is.
  • Fixed issues in grpmean(), where values instead of value labels were printed if some categories were not present in the data.

sjstats 0.14.0 2018-01-14

General

New functions

  • mcse() to compute the Monte Carlo standard error for stanreg- and brmsfit-models.
  • n_eff() to compute the effective sample size for stanreg- and brmsfit-models.

Changes to functions

  • grpmean() now uses contrasts() from package emmeans to compute p-values, which correclty indicate whether the sub-group mean is significantly different from the total mean.
  • grpmean() gets an out-argument, to print output to console, or as HTML table in the viewer or web browser.
  • tidy_stan() now includes information on the Monte Carlo standard error.
  • model_frame(), p_value() and link_inverse() now support Zelig-relogit-models.
  • typical_value() gets an explicit weight.by-argument.

Bug fixes

  • model_frame() did not work properly for variables that were standardized with scale().
  • In certain cases, weight.by-argument did not work in grpmean().

sjstats 0.13.0 2017-11-22

General

  • Remove deprecated get_model_pval().
  • Revised documentation for overdisp().

New functions

  • scale_weights() to rescale design weights for multilevel models.
  • pca() and pca_rotate() to create tidy summaries of principal component analyses or rotated loadings matrices from PCA.
  • gmd() to compute Gini’s mean difference.
  • is_prime() to check whether a number is a prime number or not.

Changes to functions

  • link_inverse() now supports brmsfit, multinom and clm-models.
  • p_value() now supports polr and multinom-models.
  • zero_count() gets a tolerance-argument, to accept models with a ratio within a certain range of 1.
  • var_names() now also cleans variable names from variables modelled with the offset(), lag() or diff() function.
  • icc(), re_var() and get_re_var() now support brmsfit-objects (models fitted with the brms-package).
  • For fun = "weighted.mean", typical_value() now checks if vector of weights is of same length as x.
  • The print-method for grpmean() now also prints the overall p-value from the model.

Bug fixes

sjstats 0.12.0 2017-10-16

General

  • Fixed examples, to resolve issues with CRAN package checks.
  • More model objects supported in p_value().

New functions

  • model_frame() to get the model frame from model objects, also of those models that don’t have a S3-generic model.frame-function.
  • var_names() to get cleaned variable names from model objects.
  • link_inverse() to get the inverse link function from model objects.

Changes to functions

  • The fun-argument in typical_value() can now also be a named vector, to apply different functions for numeric and categorical variables.

Bug fixes

  • Fixed issue with specific model formulas in pred_vars().
  • Fixed issue with specific model objects in resp_val().
  • Fixed issue with nested models in re_var().

sjstats 0.11.2 2017-09-28

New functions

Changes to functions

  • hdi() and rope() now also work for brmsfit-models, from package brms.
  • hdi() and rope() now have a type-argument, to return fixed, random or all effects for mixed effects models.

sjstats 0.11.1 2017-09-16

Changes to functions

  • typical_value() gets a “zero”-option for the fun-argument.
  • Changes to icc(), which used stats::sigma() and thus required R-version 3.3 or higher. Now should depend on R 3.2 again.
  • se() now also supports stanreg and stanfit objects.
  • hdi() now also supports stanfit-objects.
  • std_beta() gets a ci.lvl-argument, to specify the level of the calculated confidence interval for standardized coefficients.
  • get_model_pval() is now deprecated. Please use p_value() instead.

New functions

  • rope() to calculate the region of practical equivalence for MCMC samples.

sjstats 0.11.0 2017-08-21

General

  • Added vignettes for various functions.
  • Fixed issue with latest tidyr-update on CRAN.

New functions

sjstats 0.10.3 2017-07-23

New functions

  • typical_value(), to return the typical value of a variable.
  • eta_sq(), cohens_f() and omega_sq() to compute (partial) eta-squared or omega-squared statistics, or Cohen’s F for anova tables.
  • anova_stats() to compute a complete model summary, including (partial) eta-squared, omega-squared and Cohen’s F statistics for anova tables, returned as tidy data frame.
  • svy_md() as convenient shortcut to compute the median for variables from survey designs.
  • is_singular() to check a model fit for singularity in case of post-fitting convergence warnings.

Changes to functions

  • Computation of r2() for glm-objects is now based on log-Likelihood methods and also accounts for count models.
  • Better print()-method for overdisp().
  • print()-method for svyglm.nb() now also prints the dispersion parameter Theta.
  • overdisp() now supports glmmTMB-objects.
  • boot_ci() also displays CI based on sample quantiles.

Bug fixes

  • std_beta() did not work for models with only one predictor.

sjstats 0.10.2 2017-06-27

Changes to functions

Bug fixes

sjstats 0.10.1 2017-06-14

General

  • Revised imports: Labelled data functions from package sjmisc have been moved to package sjlabelled.

New functions

  • boot_est() to return the estimate from bootstrap replicates.

Changes to functions

  • The print()-method for svyglm.nb()-objects now also prints confidence intervals.

Bug fixes

  • se() did not work for icc()-objects, when the mixed model had more than one random effect term.

sjstats 0.10.0 2017-04-11

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().

sjstats 0.9.0 2017-03-06

General

  • Functions std() and center() were removed and are now in the sjmisc-package.

New functions

  • svyglm.nb() to compute survey-weighted negative binomial regressions.
  • xtab_statistics() to compute various measures of assiciation for contingency tables.
  • Added S3-model.frame()-function for gee-models.

Changes to functions

  • se() gets a type-argument, which applies to generalized linear mixed models. You can now choose to compute either standard errors with delta-method approximation for fixed effects only, or standard errors for joint random and fixed effects.

Bug fixes

  • prop() did not work for non-labelled data frames when used with grouped data frames.

sjstats 0.8.0 2017-02-03

New functions

  • svy() to compute robust standard errors for weighted models, adjusting the residual degrees of freedom to simulate sampling weights.
  • zero_count() to check whether a poisson-model is over- or underfitting zero-counts in the outcome.
  • pred_accuracy() to calculate accuracy of predictions from model fit.
  • outliers() to detect outliers in (generalized) linear models.
  • heteroskedastic() to check linear models for (non-)constant error variance.
  • autocorrelation() to check linear models for auto-correlated residuals.
  • normality() to check whether residuals in linear models are normally distributed or not.
  • multicollin() to check predictors in a model for multicollinearity.
  • check_assumptions() to run a set of model assumption checks.

Changes to functions

  • prop() no longer works within dplyr’s summarise() function. Instead, when now used with grouped data frames, a summary of proportions is directly returned as tibble.
  • se() now computes adjusted standard errors for generalized linear (mixed) models, using the Taylor series-based delta method.

sjstats 0.7.1 2016-12-18

General

  • Package depends on R-version >= 3.3.

Changes to functions

  • prop() gets a digits-argument to round the return value to a specific number of decimal places.

sjstats 0.7.0 2016-12-08

General

  • Largely revised the documentation.

New functions

  • prop() to calculate proportion of values in a vector.
  • mse() to calculate the mean square error for models.
  • robust() to calculate robust standard errors and confidence intervals for regression models, returned as tidy data frame.

sjstats 0.6.0 2016-10-31

New functions

  • split_half() to compute the split-half-reliability of tests or questionnaires.
  • sd_pop() and var_pop() to compute population variance and population standard deviation.

Changes to functions

  • se() now also computes the standard error from estimates (regression coefficients) and p-values.

sjstats 0.5.0 2016-09-26

New functions

  • Added S3-print-method for mwu()-function.
  • get_model_pval() to return a tidy data frame (tibble) of model term names, p-values and standard errors from various regression model types.
  • se_ybar() to compute standard error of sample mean for mixed models, considering the effect of clustering on the standard error.
  • std() and center() to standardize and center variables, supporting the pipe-operator.

Changes to functions

  • se() now also computes the standard error for intraclass correlation coefficients, as returned by the icc()-function.
  • std_beta() now always returns a tidy data frame (tibble) with model term names, standardized estimate, standard error and confidence intervals.
  • r2() now also computes alternative omega-squared-statistics, if null model is given.