sjstats 0.14.1

General

• Remove unused imports.
• Cross refences from dplyr::select_helpers were updated to tidyselect::select_helpers.

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

General

• Beautiful colored output for grpmean() and mwu().

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

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

• resp_val(), cv_error() and pred_accuracy() did not work for formulas with transforming function for response terms, e.g. log(response).

sjstats 0.12.0

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

New functions

• tidy_stan() to return a tidy summary of Stan-models.

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

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

General

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

New functions

• grpmean() to compute mean values by groups (One-way Anova).
• hdi() to compute high density intervals (HDI) for MCMC samples.
• find_beta() and find_beta2() to find the shape parameters of a Beta distribution.
• find_normal() and find_cauchy() to find the parameters of a normal or cauchy distribution.

sjstats 0.10.3

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

Changes to functions

• icc(), re_var() and get_re_var() now support glmmTMB-objects.
• pred_accuracy() now also reports the standard error of accuracy, and gets a print-method.

Bug fixes

• pred_accuracy() with cross-validation-method did not correctly account for the generated test data.
• Fixed issue with calculation in smpsize_lmm() and se_ybar().

sjstats 0.10.1

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

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

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

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

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

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

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

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.