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
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 = 0.1693Chi 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.