# sjstats v0.17.5

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## Collection of Convenient Functions for Common Statistical Computations

Collection of convenient functions for common statistical computations, which are not directly provided by R's base or stats packages. This package aims at providing, first, shortcuts for statistical measures, which otherwise could only be calculated with additional effort (like standard errors or root mean squared errors). Second, these shortcut functions are generic (if appropriate), and can be applied not only to vectors, but also to other objects as well (e.g., the Coefficient of Variation can be computed for vectors, linear models, or linear mixed models; the r2()-function returns the r-squared value for 'lm', 'glm', 'merMod' and other model objects). The focus of most functions lies on summary statistics or fit measures for regression models, including generalized linear models, mixed effects models and Bayesian models. However, some of the functions also deal with other statistical measures, like Cronbach's Alpha, Cramer's V, Phi etc.

# sjstats - Collection of Convenient Functions for Common Statistical Computations

Collection of convenient functions for common statistical computations, which are not directly provided by R's base or stats packages.

This package aims at providing, first, shortcuts for statistical measures, which otherwise could only be calculated with additional effort (like standard errors, Cronbach's Alpha or root mean squared errors), or for which currently no functions available.

Second, these shortcut functions are generic (if appropriate), and can be applied not only to vectors, but also to other objects as well (e.g., the Coefficient of Variation can be computed for vectors, linear models, or linear mixed models; the r2()-function returns the r-squared value for lm, glm, merMod, glmmTMB, or lme and other objects).

Most functions of this package are designed as summary functions, i.e. they do not transform the input vector; rather, they return a summary, which is sometimes a vector and sometimes a tidy data frame (where column names follow a common convention). The focus of most functions lies on summary statistics or fit measures for regression models, including generalized linear models, mixed effects models or Bayesian models. However, some of the functions deal with other statistical measures, like Cronbach's Alpha, Cramer's V, Phi etc.

The comprised tools include:

• For regression and mixed models: Coefficient of Variation, Root Mean Squared Error, Residual Standard Error, Coefficient of Discrimination, R-squared and pseudo-R-squared values, standardized beta values, p-values
• Especially for mixed models: Design effect, ICC, sample size calculation and convergence tests
• Especially for Bayesian models: Highest Density Interval, region of practical equivalence (rope), Monte Carlo Standard Errors, ratio of number of effective samples, mediation analysis, Test for Practical Equivalence
• Fit and accuracy measures for regression models: Overdispersion tests, accuracy of predictions, test/training-error comparisons, error rate and binned residual plots for logistic regression models
• For anova-tables: Eta-squared, Partial Eta-squared, Omega-squared and Partial Omega-squared statistics

Furthermore, sjstats has functions to access information from model objects, which either support more model objects than their stats counterparts, or provide easy access to model attributes, like:

• model_frame() to get the model frame,
• model_family() to get information about the model family, link functions etc.,
• link_inverse() to get the link-inverse function,
• pred_vars() and resp_var() to get the names of either the dependent or independent variables, or
• var_names() to get the "cleaned" variables names from a model object (cleaned means, things like s() or log() are removed from the returned character vector with variable names.)

Other statistics:

• Cramer's V, Cronbach's Alpha, Mean Inter-Item-Correlation, Mann-Whitney-U-Test, Item-scale reliability tests

## Documentation

Please visit https://strengejacke.github.io/sjstats/ for documentation and vignettes.

## Installation

### Latest development build

To install the latest development snapshot (see latest changes below), type following commands into the R console:

library(devtools)
devtools::install_github("strengejacke/sjstats")


Please note the package dependencies when installing from GitHub. The GitHub version of this package may depend on latest GitHub versions of my other packages, so you may need to install those first, if you encounter any problems. Here's the order for installing packages from GitHub:

### Officiale, stable release

To install the latest stable release from CRAN, type following command into the R console:

install.packages("sjstats")


## Citation

In case you want / have to cite my package, please use citation('sjstats') for citation information.

## Functions in sjstats

 Name Description cv Compute model quality chisq_gof Compute model quality is_prime Find prime numbers inequ_trend Compute trends in status inequalities bootstrap Generate nonparametric bootstrap replications mwu Mann-Whitney-U-Test boot_ci Standard error and confidence intervals for bootstrapped estimates auto_prior Create default priors for brms-models nhanes_sample Sample dataset from the National Health and Nutrition Examination Survey check_assumptions Check model assumptions se_ybar Standard error of sample mean for mixed models p_value Get p-values from regression model objects prop Proportions of values in a vector std_beta Standardized beta coefficients and CI of linear and mixed models smpsize_lmm Sample size for linear mixed models cv_error Test and training error from model cross-validation mean_n Row means with min amount of valid values deff Design effects for two-level mixed models sjstats-package Collection of Convenient Functions for Common Statistical Computations odds_to_rr Get relative risks estimates from logistic regressions or odds ratio values mediation Summary of Bayesian multivariate-response mediation-models overdisp Deprecated functions svyglm.nb Survey-weighted negative binomial generalised linear model table_values Expected and relative table values weight Weight a variable efc Sample dataset from the EUROFAMCARE project gmd Gini's Mean Difference eta_sq Effect size statistics for anova fish Sample dataset find_beta Determining distribution parameters grpmean Summary of mean values by group wtd_sd Weighted statistics for tests and variables reexports Objects exported from other packages robust Robust standard errors for regression models phi Measures of association for contingency tables scale_weights Rescale design weights for multilevel analysis se Standard Error for variables or coefficients tidy_stan Tidy summary output for stan models var_pop Calculate population variance and standard deviation No Results!

## Vignettes of sjstats

 Name anova-statistics.Rmd bayesian-statistics.Rmd mixedmodels-statistics.Rmd No Results!

## Details

 Type Package Encoding UTF-8 Date 2019-06-04 License GPL-3 URL https://github.com/strengejacke/sjstats, https://strengejacke.github.io/sjstats BugReports https://github.com/strengejacke/sjstats/issues RoxygenNote 6.1.1 VignetteBuilder knitr NeedsCompilation no Packaged 2019-06-04 12:32:17 UTC; mail Repository CRAN Date/Publication 2019-06-04 13:10:02 UTC
 imports bayestestR (>= 0.2.0) , broom , dplyr , emmeans , insight (>= 0.3.0) , lme4 , magrittr , MASS , modelr , performance (>= 0.2.0) , purrr , rlang , sjlabelled (>= 1.0.17) , sjmisc (>= 2.7.8) , tidyr suggests brms , car , coin , ggplot2 , graphics , httr , knitr , mediation , nlme , pbkrtest (>= 0.4-7) , pwr , rstan , rstanarm , sandwich , sjPlot , survey , testthat , VGAM , Zelig depends R (>= 3.2) , stats , utils Contributors