https://github.com/okgreece/gginference

Visualise the Results of Inferential Statistics using 'ggplot2'
https://github.com/okgreece/gginference

ggplot2 inferential-statistics

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Visualise the Results of Inferential Statistics using 'ggplot2'

• Host: GitHub
• URL: https://github.com/okgreece/gginference
• Owner: okgreece
• License: gpl-2.0
• Created: 2018-10-22T21:56:30.000Z (almost 8 years ago)
• Default Branch: master
• Last Pushed: 2023-06-12T13:30:40.000Z (about 3 years ago)
• Last Synced: 2025-10-22T03:55:35.601Z (9 months ago)
• Topics: ggplot2, inferential-statistics
• Language: R
• Homepage: https://okgreece.github.io/gginference/
• Size: 504 KB
• Stars: 13
• Watchers: 5
• Forks: 5
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
README

================

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# Overview

Visualise the results of F test to compare two variances, Student’s

t-test, test of equal or given proportions, Pearson’s chi-squared test

for count data and test for association/correlation between paired

samples.

# Installation

``` r

# CRAN installation:

install.packages("gginference")

# Or the development version from GitHub:

# install.packages("devtools")

devtools::install_github("okgreece/gginference")

```

# Usage

## One sample

### One sample t-test with normal population and σ2 unknown

The rejection regions for one sample t-test with normal population and

σ2 unknown are calculated using `ggttest` function. The

following table shows the rejection regions which is calculated with

**gginference** depending the specified parameters in `t.test`.

H0

H1

Rejection Region of gginference

Parameters of t.test

μ = μ0

μ < μ0

R = {z <  − za}





x = vector of sample data,



mu = μ0,



alternative = “less”



μ > μ0

R = {z > za}





x = vector of sample data,



mu = μ0,



alternative = “greater”



μ ≠ μ0

R = {|z| > za/2}





x = vector of sample data,



mu = μ0,



alternative = “two.sided”



where

![](readme_formula/1_One_sample_t-test_large.png)

### One sample t-test with normal population and n \< 30 and σ2 unknown

`ggttest` is also used to calculate rejection region for one sample

t-test with normal population and n \< 30 and σ2 unknown.

H0

H1

Rejection Region of gginference

Parameters of t.test

μ = μ0

μ < μ0

R = {t <  − tn − 1, a}





x = vector of sample data,



mu = μ0,



alternative = “less”



μ > μ0

R = {t > tn − 1, a}





x = vector of sample data,



mu = μ0,



alternative = “greater”



μ ≠ μ0

R = {|t| > tn − 1, a/2}





x = vector of sample data,



mu = μ0,



alternative = “two.sided”



where

![](readme_formula/2_One_sample_t-test_small.png)

## Two samples

### Two independent samples t-test with normal populations and σ12 = σ22 unknown

Next table shows the rejection regions of two independent samples t-test

with normal populations and σ12 =

σ22. `ggttest` is also used to visualize this

test.

H0

H1

Rejection Region of gginference

Parameters of t.test

μ1 − μ2 = d0

μ1 − μ2 < d0

R = {t <  − tn1 + n2 − 2, a}





x = vector of sample 1 data,



y = vector of sample 2 data,



paired = FALSE,



var.equal = TRUE,



alternative = “less”



μ1 − μ2 > d0

R = {t > tn1 + n2 − 2, a}





x = vector of sample 1 data,



y = vector of sample 2 data,



paired = FALSE,



var.equal = TRUE,



alternative = “greater”



μ1 − μ2 ≠ d0

R = {|t| > tn1 + n2 − 2, a/2}





x = vector of sample 1 data,



y = vector of sample 2 data,



paired = FALSE,



var.equal = TRUE,



alternative = “two.sided”



where

![](readme_formula/3_Two_samples_t-test_equal_var.png)

### Two independent samples t-test with normal populations and σ12 ![](readme_formula/uneq.png) σ22

`ggttest` is used to visualize two independent samples t-test with

normal populations and σ12

![](readme_formula/uneq.png) σ22. The following

table shows the rejection regions of this test.

H0

H1

Rejection Region of gginference

Parameters of t.test

μ1 − μ2 = d0

μ1 − μ2 < d0

R = {t <  − tν, a}





x = vector of sample 1 data,



y = vector of sample 2 data,



paired = FALSE,



var.equal = FALSE,



alternative = “less”



μ1 − μ2 > d0

R = {t > tν, a}





x = vector of sample 1 data,



y = vector of sample 2 data,



paired = FALSE,



var.equal = FALSE,



alternative = “greater”



μ1 − μ2 ≠ d0

R = {|t| > tν, a/2}





x = vector of sample 1 data,



y = vector of sample 2 data,



paired = FALSE,



var.equal = FALSE,



alternative = “two.sided”



where

![](readme_formula/4_Two_samples_t-test_unequal_var.png)

and ν degrees of freedom with

![](readme_formula/4b_Two_samples_t-test_unequal_var_deg_freed.png)

## Paired samples with normal population

`ggttest` is used also to visualize the results of the paired sample

Student’s t-test. Next table shows th rejection region of this test.

H0

H1

Rejection Region of gginference

Parameters of t.test

μ1 − μ2 = d0

μ1 − μ2 < d0

R = {t <  − tn − 1, a}





x = vector of sample 1 data,



y = vector of sample 2 data,



paired = FALSE,



var.equal = FALSE,



alternative = “less”



μ1 − μ2 > d0

R = {t > tn − 1, a}





x = vector of sample 1 data,



y = vector of sample 2 data,



paired = FALSE,



var.equal = FALSE,



alternative = “greater”



μ1 − μ2 ≠ d0

R = {|t| > tn − 1, a/2}





x = vector of sample 1 data,



y = vector of sample 2 data,



paired = FALSE,



var.equal = FALSE,



alternative = “two.sided”



where

![](readme_formula/5a_Paired%20samples.png)

![](readme_formula/5b_Paired%20samples.png)

![](readme_formula/5c_Paired%20samples.png)

## Proportion test

### One-proportion z-test

`ggproptest()` is used to visualize one-proportion z-test. The rejection

regions are shown below.

H0

H1

Rejection Region of gginference

Parameters of prop.test()

p = p0

p < p0

R = {z <  − za}





x = vector of sample data,



mu = μ0,



alternative = “less”



p > p0

R = {z > za}





x = vector of sample data,



mu = μ0,



alternative = “greater”



p ≠ p0

R = {|z| > za/2}





x = vector of sample data,



mu = μ0,



alternative = “two.sided”



where

![](readme_formula/6a_one_proportion.png)

![](readme_formula/6b_one_proportion.png)

### Two-proportion z-test

The results of two-proportion z-test are visualized using `ggproptest()`

and next table shows the rejection regions.

H0

H1

Rejection Region of gginference

Parameters of prop.test()

p1 − p2 = d0

p1 − p2 < d0

R = {z <  − za}





x = vector of sample 1 data,



y = vector of sample 2 data,



paired = FALSE,



var.equal = FALSE,



alternative = “less”



p1 − p2 > d0

R = {z > za}





x = vector of sample 1 data,



y = vector of sample 2 data,



paired = FALSE,



var.equal = FALSE,



alternative = “greater”



p1 − p2 ≠ d0

R = {|z| > za/2}





x = vector of sample 1 data,



y = vector of sample 2 data,



paired = FALSE,



var.equal = FALSE,



alternative = “two.sided”



where

![](readme_formula/7a_two_proportions.png)

![](readme_formula/7b_two_proportions.png)

![](readme_formula/7c_two_proportions.png)

![](readme_formula/7d_two_proportions.png)

## Two-sample F test for equality of variances

`ggvartest` is used to visualize the results of the paired sample

Student’s t-test. The rejection region that is used in this test is

shown below.

H0

H1

Rejection Region of gginference

Parameters of var.test

σ12 / σ22 = 1

σ12 / σ22 < 1

R = {F > Fn1 − 1, n2 − 1, 1 − a}





x = vector of sample 1 data,



y = vector of sample 2 data,



ratio = 1



alternative = “less”



σ12 / σ22 > 1

R = {F > Fn1 − 1, n2 − 1, a}





x = vector of sample data,



mu = μ0,



ratio = 1



alternative = “greater”



σ12 / σ22 ≠ 1

R = {F > Fn1 − 1, n2 − 1, a/2}





x = vector of sample data,



mu = μ0,



ratio = 1



alternative = “two.sided”



where

![](readme_formula/8_test_cor.png)

## Test for Correlation Between Paired Samples

`ggcortest` is usesd to visualize the results of test for correlation

between paired samples. The following table shows the rejection region

of this test.

H0

H1

Rejection Region of gginference

Parameters of cor.test

𝜚 = 0

𝜚 ≠ 0

R = {|t| > tn − 2, a/2}





x = vector of sample 1 data



y = vector of sample 2 data,



alternative = “two.sided”



where

![](readme_formula/8_test_cor.png)

## Chi-squared Test of Independence

The results of Pearson’s chi-squared test for count data are visulized

using `ggchisqtest`. Next table shows the rejection region of this test.

H0

H1

Rejection Region of gginference

Parameters of chisq.test

Two variables are independent

Two variables are not independent

R = {X2 > χa/22}





x = 2-dimensional contingency table



where

![](readme_formula/9_chisq.png)

## ANOVA F-test

`ggaov` is used to visualize the results of ANOVA F-test. Table below

shows rejection region of Anova F-stest.

H0

H1

Rejection Region of gginference

Parameters of aov

H0 : μ1 = μ2= ... = μk

Not all three population means are equal

R = {F > Fk − 1, n − k, a}





formula = formula specifying the model



data = data frame with the variables specified in the formula



where

![](readme_formula/10_anova.png)

# Getting help

If you encounter a bug, please feel free to open an

[issue](https://github.com/okgreece/gginference/issues) with a minimal

reproducible example.