Today, our article will give you a useful function for using statistics. It is the `cor.test()`

. The function analyzes the association between two variables and represents a report to show the information about them.

**What is the correlation test?**

In R, the correlation test refers to the analysis of the association between two variables. The correlation coefficient is calculated using Pearson, Kendall, and Spearman formulas.

**The **`cor.test()`

function in R

`cor.test()`

function in RThe `cor.test()`

function helps to carry out the correlation test based on the available formulas.

**Syntax:**

`cor.test(x, y, method, …)`

**Parameters:**

**x, y:**Two variables to make the correlation test**method:**Types of formulas to calculate the correlation coefficient. “pearson”, “kendall” or “spearman”**…:**Other parameters. For more details, please visit the official website.

**Examples of using the **`cor.test()`

function

`cor.test()`

function To feed the function, we will use the features in the Iris dataset as variables. First, take a look at the dataset.

**Code:**

head(iris)

**Result:**

```
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1 5.1 3.5 1.4 0.2 setosa
2 4.9 3.0 1.4 0.2 setosa
3 4.7 3.2 1.3 0.2 setosa
4 4.6 3.1 1.5 0.2 setosa
5 5.0 3.6 1.4 0.2 setosa
6 5.4 3.9 1.7 0.4 setosa
```

The dataset has 4 main features: the length and width of the sepal and the length and width of the petal. In the examples below, we will carry out the correlation test with two features: Petal.Length and Petal.Width.

**Measure the correlation coefficient by Pearson formula**

**Code:**

with(iris, cor.test(Petal.Length, Petal.Width, method = 'pearson'))

**Result:**

```
Pearson's product-moment correlation
data: Petal.Length and Petal.Width
t = 43.387, df = 148, p-value < 2.2e-16
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
0.9490525 0.9729853
sample estimates:
cor
0.9628654
```

The result shows that the test statistic t = 43.487, the degree of free df is 148, and the hypothesis testing p-value is 2.2e-16. The test also tells that the alternative hypothesis: true correlation is not equal to 0, and the correlation coefficient cor is 0.9628.

**Measure the correlation coefficient by Kendall formula**

**Code:**

with(iris, cor.test(Petal.Length, Petal.Width, method = 'kendall'))

**Result:**

```
Kendall's rank correlation tau
data: Petal.Length and Petal.Width
z = 13.968, p-value < 2.2e-16
alternative hypothesis: true tau is not equal to 0
sample estimates:
tau
0.8068907
```

When testing the samples with the Kendall formula, there is no confidence interval.

**Measure the correlation coefficient by Spearman formula**

**Code:**

with(iris, cor.test(Petal.Length, Petal.Width, method = 'spearman', exact = FALSE))

**Result:**

```
Spearman's rank correlation rho
data: Petal.Length and Petal.Width
S = 35061, p-value < 2.2e-16
alternative hypothesis: true rho is not equal to 0
sample estimates:
rho
0.9376668
```

Also, the test shows no confidence interval when testing with the Spearman formula.

## Summary

In summary, the `cor.test()`

function analyzes the associations of two variables based on the Pearson, Kendall, and Spearman formulas. In statistics, the function helps filter special features to get a significant result.

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My name is Robert Collier. I graduated in IT at HUST university. My interest is learning programming languages; my strengths are Python, C, C++, and Machine Learning/Deep Learning/NLP. I will share all the knowledge I have through my articles. Hope you like them.

**Name of the university: **HUST

**Major**: IT

**Programming Languages**: Python, C, C++, Machine Learning/Deep Learning/NLP