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Logistic Function in R, Here is a rewritten version of the article with the codes included:

Logistic Functions in R: A Tutorial

In this tutorial, we will explore the logistic functions in R, including the density, cumulative distribution function, quantile function, and random number generation.

We will use the `dlogis`, `plogis`, `qlogis`, and `rlogis` functions to demonstrate each of these functions.

Example 1: Logistic Density in R (dlogis Function)

To begin, we need to create a sequence of quantiles:

`x_dlogis <- seq(-10, 10, by = 0.1)`

Then, we can apply the `dlogis` function:

`y_dlogis <- dlogis(x_dlogis)`

To visualize the output, we can plot the values:

`plot(y_dlogis)`

This will produce a plot of the logistic probability density function (PDF).

Example 2: Logistic Cumulative Distribution Function (plogis Function)

For the cumulative distribution function (CDF), we need to create a sequence of quantiles:

`x_plogis <- seq(-10, 10, by = 0.1)`

Then, we can apply the `plogis` function:

`y_plogis <- plogis(x_plogis)`

To visualize the output, we can plot the values:

`plot(y_plogis)`

This will produce a plot of the logistic cumulative distribution function (CDF).

Example 3: Logistic Quantile Function (qlogis Function)

For the quantile function, we need to create a sequence of probabilities:

`x_qlogis <- seq(0, 1, by = 0.01)`

Then, we can apply the `qlogis` function:

`y_qlogis <- qlogis(x_qlogis)`

To visualize the output, we can plot the values:

`plot(y_qlogis)`

This will produce a plot of the logistic quantile function.

Example 4: Generating Random Numbers (rlogis Function)

To generate random numbers with a logistic distribution, we need to set a seed for reproducibility and a sample size:

```set.seed(123)
N <- 10000```

Then, we can apply the `rlogis` function:

`y_rlogis <- rlogis(N)`

We can print the values to the RStudio console:

`y_rlogis`

And create a histogram of the output:

`hist(y_rlogis, breaks = 70, main = "")`

This will produce a plot of the logistic density.

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