This tutorial explains how to work with the Chi-Square distribution in R using the following functions:

• dchisq: returns the value of the Chi-Square probability density function.
• pchisq: returns the value of the Chi-Square cumulative density function.
• qchisq: returns the value of the Chi-Square quantile function.
• rchisq: generates a vector of Chi-Square distributed random variables.

The following examples show how to use each of these functions in practice.

dchisq

We often use the dchisq() function with the curve() function to plot a Chi-Square distribution with a certain number of degrees of freedom.

For example, we can use the following code to plot a Chi-Square distribution with 5 degrees of freedom:

#plot Chi_Square distribution with 5 degrees of freedom
curve(dchisq(x, df=5), from=0, to=20)

The x-axis shows the values of a Chi-Square test statistic and the y-axis shows the corresponding value of the probability density function.

Related:

pchisq

We often use the pchisq() function to find the that corresponds to a given Chi-Square test statistic.

For example, suppose we perform a and end up with a test statistic of X² = 0.86404 with 2 degrees of freedom.

We can use the pchisq() function to find the p-value that corresponds to this test statistic:

#calculate p-value for given test statistic with 2 degrees of freedom
1-pchisq(0.86404, df=2)

[1] 0.6491964

The p-value turns out to be 0.6491964.

We can also confirm this is correct by using the .

qchisq

We often use the qchisq() function to find the Chi-Square critical value that corresponds to a given significance level and degrees of freedom.

For example, we can use the following code to find the Chi-Square critical value that corresponds to a significance level of .05 with 13 degrees of freedom:

qchisq(p=.95, df=13)

[1] 22.36203

The critical value turns out to be 22.36203.

We can also confirm this is correct by using the .

rchisq

We often use the rchisq() function to generate a list of n random values that follow a Chi-Square distribution with a given degrees of freedom.

For example, we can use the following code to generate a list of 1,000 random values that follow a Chi-Square distribution with 5 degrees of freedom:

#make this example reproducible
set.seed(0) 

#generate 1000 random values that follow Chi-Square dist with df=5
values <- rchisq(n=1000, df=5)

#view first five values 
head(values)

[1]  8.369701  3.130487  1.985623  5.258747 10.578594  6.360859

We can also use the hist() function to generate a histogram to visualize this distribution of values:

#create histogram to visualize distribution of values
hist(values)

The x-axis shows the data values and the y-axis shows the frequency of those values.

Additional Resources

The following tutorials explains how to work with other distributions in R: