# Half Normal Distribution in R (4 Examples)

This tutorial explains how to use the half normal distribution in R programming.

Table of contents:

Letâ€™s dive right into the examples!

## Example Data & Add-On Packages

The following data will be used as a basis for this R tutorial:

x <- seq(0, 10, by = 0.01) # Specify x-values

We also need to install and load the fdrtool package, in order to use the corresponding functions and commands:

install.packages("fdrtool") # Install & load fdrtool package library("fdrtool")

Now, we are set up and can move on to the examples!

## Example 1: Half Normal Probability Density Function (dhalfnorm Function)

In this section, Iâ€™ll illustrate how to apply the dhalfnorm function to get a half normal probability density.

Have a look at the R code below:

y_dhalfnorm <- dhalfnorm(x) # Apply dhalfnorm function

The previous syntax has created a vector of values corresponding to the half normal density.

Letâ€™s draw these data:

plot(y_dhalfnorm) # Plot dhalfnorm values

The output of the previous R programming syntax is shown in Figure 1: A half normal density plot.

## Example 2: Half Normal Cumulative Distribution Function (phalfnorm Function)

The following R syntax explains how to use the phalfnorm function to get the half normal cumulative distribution.

Once again, we can use our data object x a basis. To this data object, we can apply the phalfnorm function as shown below:

y_phalfnorm <- phalfnorm(x) # Apply phalfnorm function

Letâ€™s draw this output as well:

plot(y_phalfnorm) # Plot phalfnorm values

By executing the previous R code, we have created Figure 2, i.e. a plot of the half normal cumulative distribution.

## Example 3: Half Normal Quantile Function (qhalfnorm Function)

In this example, Iâ€™ll show how to use the half normal quantile function in the R programming language.

For this, we first need to create a new x-vector ranging from 0 to 1:

x_qhalfnorm <- seq(0, 1, by = 0.01) # Specify x-values for qhalfnorm function

Next, we can apply the qhalfnorm function to this vector:

y_qhalfnorm <- qhalfnorm(x_qhalfnorm) # Apply qhalfnorm function

Letâ€™s plot our new data:

plot(y_qhalfnorm) # Plot qhalfnorm values

As shown in Figure 3, we have created a plot of the half normal quantile function by running the previous code.

## Example 4: Generating Random Numbers (rhalfnorm Function)

The R programming syntax below demonstrates how to generate random numbers following the half normal distribution.

First, we have to set a random seed, and we have to specify the number of random numbers we want to draw:

set.seed(637452334) # Set seed for reproducibility N <- 10000 # Specify sample size

Next, we can apply the rhalfnorm function as shown below:

y_rhalfnorm <- rhalfnorm(N) # Draw N half normally distributed values head(y_rhalfnorm) # Print values to RStudio console # [1] 1.1599856 0.3692092 0.5849656 0.9449763 2.7898802 1.0108633

Letâ€™s visualize our random data:

hist(y_rhalfnorm, # Plot of randomly drawn half normal density breaks = 100, main = "")

After running the previous code the histogram shown in Figure 4 has been created.

As you can see, our random values are following the half normal distribution.

## Video, Further Resources & Summary

Do you want to know more about the usage of the half normal distribution? Then I can recommend having a look at the following video on my YouTube channel. In the video, Iâ€™m explaining the examples of this article.

*The YouTube video will be added soon.*

In addition, you could read the related tutorials on this website. You can find some articles that are related to the usage of the half normal distribution below.

- Log Normal Distribution in R
- Normal Distribution in R
- Simulate Bivariate & Multivariate Normal Distribution
- R Programming Language

You have learned in this article how to **deal with the half normal distribution** in the R programming language. If you have any further comments or questions, let me know in the comments.

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