Author: Aravind Hebbali
License: MIT
Inferential statistics allows us to make generalizations about populations using data drawn from the population. We use them when it is impractical or impossible to collect data about the whole population under study and instead, we have a sample that represents the population under study and using inferential statistics technique, we make generalizations about the population from the sample.
The inferr package:
As of version 0.1, 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")Use infer_launch_shiny_app() to explore the package using a shiny app.
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
#> ---------------------------------------------------------------------------------
#>
#> Ho: mean(write) ~=50
#>
#> Ha: mean < 50 Ha: mean ~= 50 Ha: mean > 50
#> t = 4.141 t = 4.141 t = 4.141
#> P < t = 1.0000 P > |t| = 0.0001 P > t = 0.0000infer_oneway_anova(hsb, write, prog)
#> ANOVA
#> ----------------------------------------------------------------------
#> Sum of
#> Squares DF Mean Square F Sig.
#> ----------------------------------------------------------------------
#> Between Groups 3175.698 2 1587.849 21.275 0.0000
#> 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 = 0.1693infer_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
#> ----------------------------------------------------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
#> -------------------------------------------------------------------------infer_cochran_qtest(exam, exam1, exam2, exam3)
#> Test Statistics
#> ----------------------
#> N 15
#> Cochran's Q 4.75
#> df 2
#> p value 0.093
#> ----------------------hb <-
hsb %>%
mutate(
himath = if_else(math > 60, 1, 0),
hiread = if_else(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
#> ----------------------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.