<!-- README.md is generated from README.Rmd. Please edit that file -->
## inferr: Inferential statistics with R
**Author:** [Aravind Hebbali](http://www.aravindhebbali.com)<br/>
**License:**
[MIT](https://opensource.org/licenses/MIT)
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[![](https://cranlogs.r-pkg.org/badges/grand-total/inferr)](https://cran.r-project.org/package=inferr)
## Overview
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:
- builds upon the statistical tests provided in **stats**
- provides additional and flexible input options
- more detailed and structured test results
As of version 0.1, **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
``` r
# install inferr from CRAN
install.packages("inferr")
# the development version from github
# install.packages("devtools")
devtools::install_github("rsquaredacademy/inferr")
```
## Shiny App
Use `infer_launch_shiny_app()` to explore the package using a shiny app.
## Vignettes
- [Introduction to
inferr](http://www.rsquaredacademy.com/inferr/articles/index.html)
## Usage
##### One Sample t Test
``` r
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 = 0.0000
```
##### ANOVA
``` r
infer_oneway_anova(hsb, write, prog)
#> ANOVA
#> ----------------------------------------------------------------------
#> Sum of
#> Squares DF Mean Square F Sig.
#> ----------------------------------------------------------------------
#> Between Groups 3175.698 2 1587.849 21.275 0.0000
#> 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 = 0.1693
```
##### Chi Square Test of Independence
``` r
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
``` r
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
``` r
infer_cochran_qtest(exam, exam1, exam2, exam3)
#> Test Statistics
#> ----------------------
#> N 15
#> Cochran's Q 4.75
#> df 2
#> p value 0.093
#> ----------------------
```
##### McNemar Test
``` r
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](CONDUCT.md). By participating in this project you agree to
abide by its terms.