# 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 |

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 |

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 |

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 |

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 |

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 |

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 |

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 |

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 |

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 |

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... |

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 = "") |

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:

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.

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