Logistic Distribution in R (4 Examples) | dlogis, plogis, qlogis & rlogis Functions

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

The article contains this information:

• Example 1: Logistic Density in R (dlogis Function)
• Example 2: Logistic Cumulative Distribution Function (plogis Function)
• Example 3: Logistic Quantile Function (qlogis Function)
• Example 4: Generating Random Numbers (rlogis Function)
• Video, Further Resources & Summary

Let’s take a look at some R codes in action…

Example 1: Logistic Density in R (dlogis Function)

Let’s start with the density of the logistic distribution in R. First, we have to create a sequence of quantiles:

x_dlogis <- seq(- 10, 10, by = 0.1)                     # Specify x-values for dlogis function

Then, we can insert these quantiles into the dlogis function as you can see below:

y_dlogis <- dlogis(x_dlogis)                            # Apply dlogis function

To visualize the output of the dlogis function, we can draw a plot of its output:

plot(y_dlogis)                                          # Plot dlogis values

Figure 1: Logistic Probability Density Function (PDF).

Figure 1 shows the logistic probability density function (PDF).

Example 2: Logistic Cumulative Distribution Function (plogis Function)

In Example 2, we’ll create a plot of the logistic cumulative distribution function (CDF) in R. Again, we need to create a sequence of quantiles…

x_plogis <- seq(- 10, 10, by = 0.1)                     # Specify x-values for plogis function

…that we can use as input for the plogis function:

y_plogis <- plogis(x_plogis)                            # Apply plogis function

With the plot function, we can illustrate the output of plogis:

plot(y_plogis)                                          # Plot plogis values

Figure 2: Logistic Cumulative Distribution Function (CDF).

Example 3: Logistic Quantile Function (qlogis Function)

The R programming language also provides a command for the logistic quantile function. This time we need to create a sequence of probabilities as input:

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

Now, we can use the qlogis R command to create the logistic quantile function:

y_qlogis <- qlogis(x_qlogis)                            # Apply qlogis function

As in the previous examples, we can illustrate the output with the plot function:

plot(y_qlogis)                                          # Plot qlogis values

Figure 3: Logistic Quantile Function.

Example 4: Generating Random Numbers (rlogis Function)

We can also generate a set of random numbers with a logistic distribution. First, we need to set a seed for reproducibility and a sample size of random numbers that we want to simulate:

set.seed(91929)                                         # Set seed for reproducibility
N <- 10000                                              # Specify sample size

Now, we can apply the rlogis function to draw a set of random numbers:

y_rlogis <- rlogis(N)                                   # Draw N logistically distributed values
y_rlogis                                                # Print values to RStudio console
# -0.202638116  4.253454975 -0.546483563  1.015626074  0.713380391  1.228554727 -0.469496369  0.574367616...

The following histogram shows the output of rlogis:

hist(y_rlogis,                                          # Plot of randomly drawn logis density
     breaks = 70,
     main = "")

Figure 4: Logistic Random Numbers.

Video, Further Resources & Summary

Would you like to know more about the logistic distribution in R? Then you may want to have a look at the following video of my YouTube channel. In the video instruction, I explain the content of this tutorial in RStudio.

The YouTube video will be added soon.

You could also have a look at the other tutorials on distributions and the simulation 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

Besides the video, you could have a look at the related tutorials on this website. A selection of tutorials is listed here:

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

To summarize: At this point you should know how to draw and simulate a logistic distribution in the R programming language. In case you have further comments and/or questions, tell me about it in the comments section.