Contents

- 1 Exponential Distribution probabilities using R
- 2 Exponential Distribution
- 3 Exponential probabilities using dexp() function in R
- 4 Numerical Problem for Exponential Distribution
- 5 Exponential cumulative probability using pexp() function in R
- 6 Exponential Distribution Quantiles using qexp() in R
- 7 Simulating Exponential random variable using rexp() function in R
- 8 Endnote

## Exponential Distribution probabilities using R

In this tutorial, you will learn about how to use `dexp()`

, `pexp()`

, `qexp()`

and `rexp()`

functions in R programming language to compute the individual probabilities, cumulative probabilities, quantiles and to generate random sample for Exponential distribution.

Before we discuss R functions for Exponential distribution, let us see what is Exponential distribution.

## Exponential Distribution

Exponential distribution distribution is a continuous type probability distribution.

Exponential distribution is often used to model the lifetime of electric components. It is routinely used as a survival distribution in survival analysis and reliability analysis.

Let $X\sim Exp(\lambda)$. Then the probability distribution of $X$ is

` $$ \begin{aligned} f(x)&= \begin{cases} \lambda e^{-\lambda x}, & x > 0;\lambda> 0; \\ 0, & Otherwise. \end{cases} \end{aligned} $$ `

where $\lambda$ is the scale parameter (also known as `rate`

) of Exponential distribution.

Read more about the theory and results of Exponential distribution here.

## Exponential probabilities using `dexp()`

function in R

For continuous probability distribution, density is the value of the probability density function at $x$ (i.e., $f(x)$).

The syntax to compute the probability density function for Exponential distribution using R is

`dexp(x,rate=1)`

where

`x`

: the value(s) of the variable and,`rate`

: rate parameter of exponential distribution.

**Note:** If you do not specify the `rate`

, R assumes the default value `rate=1`

(which is a standard exponential distribution).

The `dexp()`

function gives the density for given value(s) `x`

and `rate`

.

## Numerical Problem for Exponential Distribution

To understand the four functions `dexp()`

, `pexp()`

, `qexp()`

and `rexp()`

, let us take the following numerical problem.

### Exponential Distribution Example

The time (in hours) required to repair a machine is an exponential distributed random variable with paramter $\lambda=1/2$.

(a) Find the value of the density function at $x=2.5$.

(b) Plot the graph of Exponential probability distribution.

(c) Find the probability that a repair time takes at most 3 hours.

(d) Find the probability that a repair time exceeds 4 hours.

(e) Find the probability that a repair time takes between 2 to 4 hours.

(f) Plot the graph of cumulative Exponential probabilities.

(g) What is the value of $c$, if $P(X\leq c) \geq 0.50$?

(h) Simulate 1000 Exponential distributed random variables with $\lambda= 1/2$.

Let $X$ denote the time (in hours) required to repair a machine. Given that $X\sim Exp(\lambda=1/2)$.

### Example 1: How to use ` dexp()`

function in R?

To find the value of the density function at $x=2.5$ we need to use ` dexp()`

function.

First let us define the given parameters as

```
# scale parameter
lambda <- 1/2
```

The probability density function of $X$ is

` $$ \begin{aligned} f(x)&= \frac{1}{2} e^{-x/2},\\ &\quad\text{for } x \geq 0. \end{aligned} $$ `

For part (a), we need to find the density function at $x=2.5$. That is $f(2.5)$.

(a) The value of the density function at $x=2.5$ is

` $$ \begin{aligned} f(2.5)&= \frac{1}{2}\times e^{-2.5/2}\\ &=2.5\times e^{-1.25}\\ &= 0.1432524 \end{aligned} $$ `

The above probability can be calculated using `dexp(2.5,rate=0.5)`

function in R.

```
# Compute Exponential probability
result1 <- dexp(2.5,rate=lambda)
result1
```

`[1] 0.1432524`

### Example 2 Visualize Exponential probability distribution

Using `dexp()`

function we can compute Exponential distribution probabilities for given `x`

and `rate`

. To plot the probability density function of Exponential distribution, we need to create a sequence of `x`

values and compute the corresponding probabilities.

```
# create a sequence of x values
x <- seq(0,5, by=0.02)
## Compute the Exponential pdf for each x
px<- dexp(x,rate=lambda)
```

(b) Visualizing Exponential Distribution with `dexp()`

function and `plot()`

function in R:

The probability density function of Exponential distribution with given 0.5 can be visualized using `plot()`

function as follows:

```
## Plot the Exponential probability dist
plot(x,px,type="l",xlim=c(0,5),ylim=c(0,max(px)),
lwd=3, col="darkred",ylab="f(x)")
title("PDF of Exponential (lambda = 1/2)")
```

## Exponential cumulative probability using `pexp()`

function in R

The syntax to compute the cumulative probability distribution function (CDF) for Exponential distribution using R is

`pexp(q, rate=1)`

where

`q`

: the value(s) of the variable,`rate`

: scale parameter of exponential distribution.

Using this function one can calculate the cumulative distribution function of Exponential distribution for given value(s) of `q`

(value of the variable `x`

), `rate`

.

### Example 3: How to use `pexp()`

function in R?

In the above example, for part (c), we need to find the probability $P(X\leq 3)$.

(c) The probability that a repair time takes at most 3 hours is

` $$ \begin{aligned} P(X\leq 3) &=\int_0^{3} f(x)\; dx. \end{aligned} $$ `

```
## Compute cumulative Exponential probability
result2 <- pexp(3,rate=lambda)
result2
```

`[1] 0.7768698`

### Example 4: How to use `pexp()`

function in R?

In the above example, for part (d), we need to find the probability $P(X \geq 4)$.

To calculate the probability that a random variable $X$ is greater than a given number, one can use the option `lower.tail=FALSE`

in `pexp()`

function.

Above probability can be calculated easily using `pexp()`

function with argument `lower.tail=FALSE`

as

$P(X \geq 4) =\int_{4}^\infty f(x)\; dx$= `pexp(4,rate=lambda,lower.tail=FALSE)`

or by using complementary event as

$P(X \geq 4) = 1- P(X\leq 4)$= 1- `pexp(4,rate=lambda)`

```
# compute cumulative Exponential probabilities
# with lower.tail False
pexp(4,rate=lambda,lower.tail=FALSE)
```

`[1] 0.1353353`

(d) The probability that a repair time exceeds 4 hours is

` $$ \begin{aligned} P(X\geq 4) &=\int_{4}^\infty f(x)\; dx\\ &=0.1353353. \end{aligned} $$ `

```
# Using complementary event
1-pexp(4,rate=lambda)
```

`[1] 0.1353353`

### Example 5: How to use `pexp()`

function in R?

One can also use `pexp()`

function to calculate the probability that the random variable $X$ is between two values.

(e) The probability that a repair time takes between 2 to 4 hours can be written as $P(2 < X < 4)$.

` $$ \begin{aligned} P(2 < X < 4) &= P(X< 4) -P(X < 2)\\ &= 0.8646647 - 0.6321206\\ &= 0.2325442 \end{aligned} $$ `

The above probability can be calculated using `pexp()`

function as follows:

```
a <- pexp(4,rate=lambda)
b <- pexp(2,rate=lambda)
result3 <- a - b
result3
```

`[1] 0.2325442`

### Example 6: Visualize the cumulative Exponential probability distribution

Using `pexp()`

function we can compute Exponential cumulative probabilities (CDF) for given `x`

and `rate`

. To plot the CDF of Exponential distribution, we need to create a sequence of `x`

values and compute the corresponding cumulative probabilities.

```
# create a sequence of x values
x <- seq(0,5, by=0.02)
## Compute the Exponential pdf for each x
Fx <- pexp(x,rate=lambda)
```

(f) Visualizing Exponential Distribution with `pexp()`

function and `plot()`

function in R:

The cumulative probability distribution of Exponential distribution with given `x`

and `rate`

can be visualized using `plot()`

function as follows:

```
## Plot the Exponential probability dist
plot(x,Fx,type="l",xlim=c(0,5),ylim=c(0,1),
lwd=3, col="darkred",ylab="F(x)")
title("CDF of Exp (lambda= 1/2)")
```

## Exponential Distribution Quantiles using `qexp()`

in R

The syntax to compute the quantiles of Exponential distribution using R is

`qexp(p,rate=1)`

where

`p`

: the value(s) of the probabilities,`rate =1`

: scale parameter of exponential distribution.

The function `qexp(p,rate=1)`

gives $100*p^{th}$ quantile of Exponential distribution for given value of `p`

, and `rate`

.

The $p^{th}$ quantile is the smallest value of Exponential random variable $X$ such that $P(X\leq x) \geq p$.

It is the inverse of `pexp()`

function. That is, inverse cumulative probability distribution function for Exponential distribution.

### Example 7: How to use `qexp()`

function in R?

In part (g), we need to find the value of $c$ such a that $P(X\leq c) \geq 0.50$. That is we need to find the $50^{th}$ quantile of given Exponential distribution.

```
lambda <- 1/2
prob <- 0.50
```

```
# compute the quantile for Exponential dist
qexp(0.50,rate=lambda)
```

`[1] 1.386294`

The $50^{th}$ percentile of given Exponential distribution is 1.3862944.

### Visualize the quantiles of exponential Distribution

The quantiles of exponential distribution with given `p`

and `rate=lambda`

can be visualized using `plot()`

function as follows:

```
p <- seq(0,1,by=0.02)
qx <- qexp(p,rate=lambda)
# Plot the Quantiles of Exponential dist
plot(p,qx,type="l",lwd=2,col="darkred",
ylab="quantiles",
main="Quantiles of Exponential(lambda=1/2)")
```

## Simulating Exponential random variable using `rexp()`

function in R

The general R function to generate random numbers from Exponential distribution is

`rexp(n,rate=1)`

where,

`n`

: the sample observations,`rate`

: scale parameter of exponential distribution.

The function `rexp(n,rate)`

generates `n`

random numbers from Exponential distribution with given `rate`

.

### Example 8: How to use `rexp()`

function in R?

In part (h), we need to generate 1000 random numbers from Exponential distribution with given $rate = 0.5$.

(h) We can use `rexp(1000,rate)`

function to generate random numbers from Exponential distribution.

```
## initialize sample size to generate
n <- 1000
# Simulate 1000 values From Exponential dist
x_sim <- rexp(n,rate=lambda)
```

The below graphs shows the density of the simulated random variables from Exponential Distribution.

```
## Plot the simulated data
plot(density(x_sim),xlab="Simulated x",ylab="density",
lwd=5,col="darkred",
main="Simulated data from Exponential(lambda=1/2) dist")
```

If you use same function again, R will generate another set of random numbers from $Exp(0.5)$.

```
# Simulate 1000 values From Exponential dist
x_sim_2 <- rexp(n,rate=lambda)
```

```
## Plot the simulated data
plot(density(x_sim_2),xlab="Simulated x",ylab="density",
lwd=5,col="blue",
main="Simulated data from Exp(lambda=1/2) dist")
```

For the simulation purpose to reproduce same set of random numbers, one can use `set.seed()`

function.

```
# set seed for reproducibility
set.seed(1457)
# Simulate 1000 values From Exponential dist
x_sim_3 <- rexp(n,rate=lambda)
```

```
## Plot the simulated data
plot(density(x_sim_3),xlab="Simulated x",ylab="density",
lwd=5,col="darkred",
main="Simulated data from Exp(lambda=1/2) dist")
```

```
set.seed(1457)
# Simulate 1000 values From Exponential dist
x_sim_4 <- rexp(n,rate=lambda)
```

```
## Plot the simulated data
plot(density(x_sim_4),xlab="Simulated x",ylab="density",
lwd=5,col="darkred",
main="Simulated data from Exp(lambda=1/2) dist")
```

Since we have used `set.seed(1457)`

function, R will generate the same set of Exponential distributed random numbers.

`hist(x_sim_4,breaks = 30)`

To learn more about other discrete and continuous probability distributions using R, go through the following tutorials:

**Discrete Distributions Using R**

Binomial distribution in R

Poisson distribution in R

Geometric distribution in R

Negative Binomial distribution in R

Hypergeometric distribution in R

**Continuous Distributions Using R**

Uniform distribution in R

Normal distribution in R

Log-Normal distribution in R

Beta distribution in R

Gamma distribution in R

Cauchy distribution in R

Laplace distribution in R

Logistic distribution in R

Weibull distribution in R

## Endnote

In this tutorial, you learned about how to compute the probabilities, cumulative probabilities and quantiles of Exponential distribution in R programming. You also learned about how to simulate a Exponential distribution using R programming.

To learn more about R code for discrete and continuous probability distributions, please refer to the following tutorials:

Probability Distributions using R

Let me know in the comments below, if you have any questions on Exponential Distribution using R and your thought on this article.