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.

Readme

sjstats - Collection of Convenient Functions for Common Statistical Computations

DOI

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:

sjlabelledsjmiscsjstatsggeffectssjPlot

Officiale, stable release

CRAN_Status_Badge    downloads    total

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.

DOI

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
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Vignettes of sjstats

Name
anova-statistics.Rmd
bayesian-statistics.Rmd
mixedmodels-statistics.Rmd
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