Weibull Distribution in R (4 Examples) | dweibull, pweibull, qweibull & rweibull Functions

In this tutorial you’ll learn how to apply the weibull functions in R.

Table of contents:

• Example 1: Weibull Density in R (dweibull Function)
• Example 2: Weibull Distribution Function (pweibull Function)
• Example 3: Weibull Quantile Function (qweibull Function)
• Example 4: Random Number Generation (rweibull Function)
• Video, Further Resources & Summary

Let’s get started:

Example 1: Weibull Density in R (dweibull Function)

In Example 1, we will create a plot representing the weibull density.

First, we need to create some x-values, for which we want to return the corresponding values of the weibull density:

x_dweibull <- seq(- 5, 30, by = 1)                    # Specify x-values for dweibull function

Now, we can apply the dweibull function of the R programming language to return the corresponding value of the weibull density with a shape of 0.1 and a scale of 1 for each of our input values:

y_dweibull <- dweibull(x_dweibull, shape = 0.1)       # Apply dweibull function

We can create a graphic showing these values with the plot function:

plot(y_dweibull)                                      # Plot dweibull values

Figure 1: Weibull Density in R Plot.

Figure 1 illustrates the weibull density for a range of input values between -5 and 30 for a shape of 0.1 and a scale of 1.

Example 2: Weibull Distribution Function (pweibull Function)

In the second example, we’ll create the cumulative distribution function (CDF) of the weibull distribution.

Again, we need to specify a vector of input values:

x_pweibull <- seq(- 5, 30, by = 1)                    # Specify x-values for pweibull function

Now, we can apply the pweibull R command in order to return the corresponding CDF value for each input value…

y_pweibull <- pweibull(x_pweibull, shape = 0.1)       # Apply pweibull function

…and then we can draw a plot containing these values in R:

plot(y_pweibull)                                      # Plot pweibull values

Figure 2: Cumulative Distribution Function According to Weibull Distribution.

Example 3: Weibull Quantile Function (qweibull Function)

Next, we will create a plot representing the weibull quantile function.

Let’s create a sequence of values between 0 and 1, for which we want to return the corresponding value of the quantile function:

x_qweibull <- seq(0, 1, by = 0.02)                    # Specify x-values for qweibull function

Now, we can use the qweibull R function to return the values of the quantile function:

y_qweibull <- qweibull(x_qweibull, shape = 0.1)       # Apply qweibull function

The following R code produces the corresponding scatterplot:

plot(y_qweibull)                                      # Plot qweibull values

Figure 3: Weibull Quantile Function.

Example 4: Random Number Generation (rweibull Function)

We can also draw random values according to the weibull density.

First, we need to specify a seed and a sample size of random numbers:

set.seed(13579)                                       # Set seed for reproducibility
N <- 100                                              # Specify sample size

Now, we can use the rweibull command to draw a set of random numbers:

y_rweibull <- rweibull(N, shape = 0.1)                # Draw N weibull distributed values
y_rweibull                                            # Print values to RStudio console
# 2.924615e+03 1.248956e-09 3.362811e+03 1.392134e-10 4.235278e-01 3.332413e+00 2.545625e+04

The RStudio console output is showing the result of the previous R syntax.

We can also produce a density plot of these numbers:

plot(density(y_rweibull),                             # Plot of randomly drawn weibull density
     main = "Weibull Distribution in R")

Figure 4: Random Weibull Numbers.

Video, Further Resources & Summary

Do you need more information on the R code of this tutorial? Then you may want to have a look at the following video of my YouTube channel. I’m explaining the R programming codes of this tutorial in the video:

The YouTube video will be added soon.

You could also read the other pages on distributions and the generation of random numbers in R:

• Bernoulli Distribution in R
• Beta Distribution in R
• Binomial Distribution in R
• Bivariate & Multivariate Distributions in R
• Cauchy Distribution in R
• Chi-Squred Distribution in R
• Exponential Distribution in R
• F Distribution in R
• Gamma Distribution in R
• Geometric Distribution in R
• Hypergeometric Distribution in R
• Log Normal Distribution in R
• Logistic Distribution in R
• Negative Binomial Distribution in R
• Normal Distribution in R
• Poisson Distribution in R
• Student t Distribution in R
• Studentized Range Distribution in R
• Uniform Distribution in R
• Weibull Distribution in R
• Wilcoxon Signedank Statistic Distribution in R
• Wilcoxonank Sum Statistic Distribution in R

Additionally, you may have a look at some of the related articles of this homepage.

• R Graphics Gallery
• R Functions List (+ Examples)
• The R Programming Language

In this post you learned some basics of the weibull distribution in R. Don’t hesitate to let me know in the comments section, in case you have additional questions.