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Tools for Statistical Inference
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:
# install inferr from CRAN
install.packages("inferr")
# the development version from github
# install.packages("devtools")
devtools::install_github("rsquaredacademy/inferr")ifr_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
#> --------------------------------------------------------------------------------ifr_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 = 0.1693ifr_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
#> ----------------------------------------------------ifr_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
#> -------------------------------------------------------------------------ifr_cochran_qtest(exam, exam1, exam2, exam3)
#> Test Statistics
#> ----------------------
#> N 15
#> Cochran's Q 4.75
#> df 2
#> p value 0.093
#> ----------------------hb <- hsb
hb$himath <- ifelse(hsb$math > 60, 1, 0)
hb$hiread <- ifelse(hsb$read > 60, 1, 0)
ifr_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
#> ----------------------If you encounter a bug, please file a minimal reproducible example using reprex on github. For questions and clarifications, use StackOverflow.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.