rsquaredacademy / inferr

Parametric and non-parametric statistical tests

inferr

Tools for Statistical Inference

Overview

inferr builds upon the statistical tests provided in stats, provides additional and flexible input options and more detailed and structured test results. As of version 0.3, inferr includes a select set of parametric and non-parametric statistical tests which are listed below:

• One Sample t Test
• Paired Sample t Test
• Independent Sample t Test
• One Sample Proportion Test
• Two Sample Proportion Test
• One Sample Variance Test
• Two Sample Variance Test
• Binomial Test
• ANOVA
• Chi Square Goodness of Fit Test
• Chi Square Independence Test
• Levene’s Test
• Cochran’s Q Test
• McNemar Test
• Runs Test for Randomness

Installation

# install inferr from CRAN
install.packages("inferr")

# the development version from github
# install.packages("devtools")
devtools::install_github("rsquaredacademy/inferr")

Articles

• Introduction to inferr

Usage

One Sample t Test

infer_os_t_test(hsb, write, mu = 50, type = 'all')
#>                               One-Sample Statistics                               
#> ---------------------------------------------------------------------------------
#>  Variable    Obs     Mean     Std. Err.    Std. Dev.    [95% Conf. Interval] 
#> ---------------------------------------------------------------------------------
#>   write      200    52.775     0.6702       9.4786       51.4537    54.0969   
#> ---------------------------------------------------------------------------------
#> 
#>                                   Two Tail Test                                  
#>                                  ---------------                                  
#> 
#>                                Ho: mean(write) ~=50                              
#>                                Ha: mean(write) !=50                               
#> --------------------------------------------------------------------------------
#>  Variable      t      DF       Sig       Mean Diff.    [95% Conf. Interval] 
#> --------------------------------------------------------------------------------
#>   write      4.141    199    0.00005       2.775         1.4537     4.0969   
#> --------------------------------------------------------------------------------

ANOVA

infer_oneway_anova(hsb, write, prog)
#>                                 ANOVA                                  
#> ----------------------------------------------------------------------
#>                    Sum of                                             
#>                    Squares     DF     Mean Square      F        Sig.  
#> ----------------------------------------------------------------------
#> Between Groups    3175.698      2      1587.849      21.275      0    
#> Within Groups     14703.177    197      74.635                        
#> Total             17878.875    199                                    
#> ----------------------------------------------------------------------
#> 
#>                  Report                   
#> -----------------------------------------
#> Category     N       Mean      Std. Dev. 
#> -----------------------------------------
#>    1        45      51.333       9.398   
#>    2        105     56.257       7.943   
#>    3        50      46.760       9.319   
#> -----------------------------------------
#> 
#> Number of obs = 200       R-squared     = 0.1776 
#> Root MSE      = 8.6392    Adj R-squared = 0.1693

Chi Square Test of Independence

infer_chisq_assoc_test(hsb, female, schtyp)
#>                Chi Square Statistics                 
#> 
#> Statistics                     DF    Value      Prob 
#> ----------------------------------------------------
#> Chi-Square                     1    0.0470    0.8284
#> Likelihood Ratio Chi-Square    1    0.0471    0.8282
#> Continuity Adj. Chi-Square     1    0.0005    0.9822
#> Mantel-Haenszel Chi-Square     1    0.0468    0.8287
#> Phi Coefficient                     0.0153          
#> Contingency Coefficient             0.0153          
#> Cramer's V                          0.0153          
#> ----------------------------------------------------

Levene’s Test

infer_levene_test(hsb, read, group_var = race)
#>            Summary Statistics             
#> Levels    Frequency    Mean     Std. Dev  
#> -----------------------------------------
#>   1          24        46.67      10.24   
#>   2          11        51.91      7.66    
#>   3          20        46.8       7.12    
#>   4          145       53.92      10.28   
#> -----------------------------------------
#> Total        200       52.23      10.25   
#> -----------------------------------------
#> 
#>                              Test Statistics                              
#> -------------------------------------------------------------------------
#> Statistic                            Num DF    Den DF         F    Pr > F 
#> -------------------------------------------------------------------------
#> Brown and Forsythe                        3       196      3.44    0.0179 
#> Levene                                    3       196    3.4792     0.017 
#> Brown and Forsythe (Trimmed Mean)         3       196    3.3936     0.019 
#> -------------------------------------------------------------------------

Cochran’s Q Test

infer_cochran_qtest(exam, exam1, exam2, exam3)
#>    Test Statistics     
#> ----------------------
#> N                   15 
#> Cochran's Q       4.75 
#> df                   2 
#> p value          0.093 
#> ----------------------

McNemar Test

hb <- hsb
hb$himath <- ifelse(hsb$math > 60, 1, 0)
hb$hiread <- ifelse(hsb$read > 60, 1, 0)
infer_mcnemar_test(hb, himath, hiread)
#>            Controls 
#> ---------------------------------
#> Cases       0       1       Total 
#> ---------------------------------
#>   0        135      21        156 
#>   1         18      26         44 
#> ---------------------------------
#> Total      153      47        200 
#> ---------------------------------
#> 
#>        McNemar's Test        
#> ----------------------------
#> McNemar's chi2        0.2308 
#> DF                         1 
#> Pr > chi2              0.631 
#> Exact Pr >= chi2      0.7493 
#> ----------------------------
#> 
#>        Kappa Coefficient         
#> --------------------------------
#> Kappa                     0.4454 
#> ASE                        0.075 
#> 95% Lower Conf Limit      0.2984 
#> 95% Upper Conf Limit      0.5923 
#> --------------------------------
#> 
#> Proportion With Factor 
#> ----------------------
#> cases             0.78 
#> controls         0.765 
#> ratio           1.0196 
#> odds ratio      1.1667 
#> ----------------------

Getting Help

If you encounter a bug, please file a minimal reproducible example using reprex on github. For questions and clarifications, use StackOverflow.

Code of Conduct

Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.

inferr's Issues

Binomial test accepts non-binary variables

The binomial test should throw an error when the input is a non binary variable.

Error in binomial test

Error in computation of two sided p-value in binomial test.

One sample kurtosis test

Syntax

ifr_os_kurtosis_test()

Description

Does a sample of n subjects (or objects) come from a population distribution that is mesokurtic?

Independent sample t test

The independent sample t test should throw an error when the grouping variable has more than 2 levels.

Incorrect package alias

From CRAN:

Dear maintainer,

You have file 'inferr/man/inferr.Rd' with \docType{package}, likely
intended as a package overview help file, but without the appropriate
PKGNAME-package \alias as per "Documenting packages" in R-exts.

This seems to be the consequence of the breaking change

  Using @docType package no longer automatically adds a -package alias.
  Instead document _PACKAGE to get all the defaults for package
  documentation.

in roxygen2 7.0.0 (2019-11-12) having gone unnoticed, see
<https://github.com/r-lib/roxygen2/issues/1491>.

As explained in the issue, to get the desired PKGNAME-package \alias
back, you should either change to the new approach and document the new
special sentinel

  "_PACKAGE"

or manually add

  @aliases inferr-package

if remaining with the old approach.

Please fix in your master sources as appropriate, and submit a fixed
version of your package within the next few months.

Best,
-k

Visualize statistical significance

Where possible, add a plot method to tests. To do:

• look at examples such as this
• shortlist tests

References

• https://petemiksza.com/visualizing-statistical-concepts/
• http://www.artofstat.com/webapps.html
• https://www.ncss.com/software/pass/pass-documentation/

Update shiny app

Update app with the new api

Friedman Test

Refer to the following:

• https://stat.ethz.ch/R-manual/R-devel/library/stats/html/friedman.test.html

2 sample proportion test

The 2 sample proportion test should accept only binary variables.

Error in devel version

Error detected in devel version of R (check here)

Kruskal Wallis Test

Refer to the following:

• https://www.stata.com/manuals/rkwallis.pdf
• https://stat.ethz.ch/R-manual/R-devel/library/stats/html/kruskal.test.html

Wilcoxon Test

Refer to the following:

• stata.com/manuals/rsignrank.pdf
• https://stat.ethz.ch/R-manual/R-devel/library/stats/html/wilcox.test.html

One sample z test

Syntax

ifr_os_z_test()

Description

Does a sample of n subjects (or objects) come from a population in which the mean (μ) equals a specified value?

Return test statistics as tibble

Return the test statistics as a tibble.

2 sample variance test

The 2 sample variance test should accept only binary variables for the group_var input. The number of continuous variables must also not exceed two.

README template

Use the standard template for README:

• Overview
• Installation
• Shiny App
• Usage
• Articles
• Features
• Getting Help
• Code of Conduct

0.3.1 Release Checklist

• Check current CRAN check results
• Polish NEWS
• devtools::build_readme()
• urlchecker::url_check()
• devtools::check(remote = TRUE, manual = TRUE)
• devtools::check_win_devel()
• rhub::check_for_cran()
• rhub::check(platform = 'ubuntu-rchk')
• rhub::check_with_sanitizers()
• revdepcheck::revdep_check(num_workers = 4)
• Update cran-comments.md
• Review pkgdown reference index for, e.g., missing topics

Submit to CRAN:

• Bump version in DESCRIPTION and NEWS
• devtools::submit_cran()
• Approve email

Wait for CRAN...

• Accepted 🎉
• Tag release
• Bump dev version in DESCRIPTION and NEWS
• Update install instructions in README

Use rlang for errors and warnings

Use rlang equivalents for errors, warnings and messages.

Documentation

Add the following:

• Contributing Guide
• Issue Template

Return current version

Add a function to return the current version of the package on CRAN and GitHub.

Error Messages

User friendly error messages (check here).

Remove shiny app

Move shiny app to xplorerr package.

Independent sample t test

The input for independent sample t test is restricted to 1 continuous variable and 1 categorical variable. The input must be flexible and accommodate 2 continuous variables as well.

Power and sample size

Syntax

• ifr_power()
• ifr_sample_size()

Power and sample size computation for the following test:

• infer_oneway_anova()
• infer_os_prop_test()
• infer_os_t_test()
• infer_os_var_test()
• infer_ts_ind_ttest()
• infer_ts_paired_ttest()
• infer_ts_prop_test()
• infer_ts_prop_grp()
• infer_ts_prop_calc()
• infer_ts_var_test()

Migrate to testthat 3

Currently, output from print functions are tested using expect_output() . Migrating to edition 3 will allow snapshot based testing.

Migrate github actions to version 2

ANOVA: Post Hoc Multiple Comparisons

Equal variances assumed:

• LSD
• Bonferroni
• Sidak
• Scheffe
• Tukey
• Tukey's-b
• Duncan
• Gabriel

Equal variances not assumed:

• Tamhane's T2
• Dunnett's T3
• Games-Howell
• Dunnett's C

Wilcoxon rank sum test

Refer to the following:

• https://www.stata.com/manuals13/rranksum.pdf
• https://stat.ethz.ch/R-manual/R-devel/library/stats/html/wilcox.test.html

Add interactive tutorial

Add an interactive tutorial using learnr.

One sample skewness test

Syntax

ifr_os_skew_test()

Description

Does a sample of n subjects (or objects) come from a population distribution that is symmetrical (i.e., not skewed)?

Unequal sample sizes

The current API design does not take into account unequal sample sizes in case of two sample tests. This must be fixed on a priority basis.

• infer_ts_ind_ttest()
• infer_ts_var_test()