## The exponential distribution

The exponential distribution is the probability distribution of the time or space between two events in a Poisson process, where the events occur continuously and independently at a constant rate \lambda.

It is important to know the probability density function, the distribution function and the quantile function of the exponential distribution. Let X \sim Exp(\lambda), that is to say, a random variable with exponential distribution with rate \lambda:

- The
**probability density function**(PDF) of x is f(x) = \lambda e^{- \lambda x} if x \geq 0 or 0 otherwise. - The
**cumulative distribution function**(CDF) is F(x) = P(X \leq x). - The
**quantile function**is Q(p) = F^{-1}(p) = \frac{-ln (1 - p)}{\lambda}. - The
**expected mean and variance**of X are E(X) = \frac{1}{\lambda} and Var(X) = \frac{(b-a)^2}{12}, respectively.

In R, the previous functions can be calculated with the `dexp`

, `pexp`

and `qexp`

functions. In addition, the `rexp`

function allows obtaining random observations following an exponential distribution. The functions are described in the following table:

Function | Description |
---|---|

dexp | Exponential density (Probability density function) |

pexp | Exponential distribution (Cumulative distribution function) |

qexp | Quantile function of the exponential distribution |

rexp | Exponential random number generation |

You can see the relationship between the three first functions in the following plot for \lambda = 1:

## The dexp function

The function in R to calculate the density function for any rate \lambda is the `dexp`

function, described below:

```
dexp(x, # X-axis values (> 0)
rate = 1, # Vector of rates (lambdas)
log = FALSE) # If TRUE, probabilities are given as log
```

As an example, if you want to calculate the exponential density function of rate 2 for a grid of values in R you can type:

```
# Grid of values
x <- seq(from = 0, to = 8, by = 0.01)
# Exponential PDF of rate 2
dexp(x, rate = 2)
```

However, recall that **the rate is not the expected value**, so if you want to calculate, for instance, an exponential distribution in R with mean 10 you will need to calculate the corresponding rate:

```
# Exponential density function of mean 10
dexp(x, rate = 0.1) # E(X) = 1/lambda = 1/0.1 = 10
```

### Plot exponential density in R

With the output of the `dexp`

function you can plot the density of an exponential distribution. For that purpose, you need to pass the grid of the X axis as first argument of the `plot`

function and the `dexp`

as the second argument. In the following block of code we show you how to plot the density functions for \lambda = 1 and \lambda = 2.

```
# Grid of X-axis values
x <- seq(0, 8, 0.1)
# lambda = 2
plot(x, dexp(x, 2), type = "l",
ylab = "", lwd = 2, col = "red")
# lambda = 1
lines(x, dexp(x, rate = 1), col = "blue", lty = 1, lwd = 2)
# Adding a legend
legend("topright", c(expression(paste(, lambda)), "2", "1"),
lty = c(0, 1, 1), col = c("blue", "red"), box.lty = 0, lwd = 2)
```

## The pexp function

The R function that allows you to calculate the probabilities of a random variable X taking values lower than x is the `pexp`

function, that has the following syntax:

```
pexp(q,
rate = 1,
lower.tail = TRUE, # If TRUE, probabilities are P(X <= x), or P(X > x) otherwise
log.p = FALSE)
```

For instance, the probability of the variable (of rate 1) taking a value lower or equal to 2 is 0.8646647:

`pexp(2) # 0.8646647`

### pexp example: calculating exponential probabilities

The time spent on a determined web page is known to have an exponential distribution with an average of 5 minutes per visit. In consequence, as E(X) = \frac{1}{\lambda}; 5 = \frac{1}{\lambda}; \lambda = 0.2.

First, if you want to calculate the probability of a visitor spending up to 3 minutes on the site you can type:

```
pexp(3, rate = 0.2) # 0.4511884 or 45.12%
1 - pexp(3, rate = 0.2, lower.tail = FALSE) # Equivalent
```

In order to plot the area under an exponential curve with a single line of code you can use the following function that we have developed:

```
# rate: lambda parameter
# lb: lower bound of the area
# ub: upper bound of the area
# acolor: color of the area
# ...: additional arguments to be passed to the lines function
exp_area <- function(rate = 1, lb, ub, acolor = "lightgray", ...) {
x <- seq(0, 12/rate, 0.01)
if (missing(lb)) {
lb <- min(x)
}
if (missing(ub)) {
ub <- max(x)
}
x2 <- seq(lb, ub, length = 100)
plot(x, dexp(x, rate = rate), type = "n", ylab = "")
y <- dexp(x2, rate = rate)
polygon(c(lb, x2, ub), c(0, y, 0), col = acolor)
lines(x, dexp(x, rate = rate), type = "l", ...)
}
```

As an example, you could plot the area under an exponential curve of rate 0.5 between 0.5 and 5 with the following code:

`exp_area(rate = 0.5, lb = 0.5, ub = 5, acolor = "white")`

The calculated probability (45.12%) corresponds to the following area:

```
exp_area(rate = 0.2, ub = 3, acolor = rgb(0, 0, 1, alpha = 0.5))
text(1, 0.075, "45.12%", srt = 90, col = "white", cex = 1.2)
```

Second, if you want to calculate the probability of a visitor spending more than 10 minutes on the site you can type:

`pexp(10, rate = 0.2, lower.tail = FALSE) # 0.1353353 or 13.53%`

The area that corresponds to the previous probability can be plotted with the following code:

```
exp_area(rate = 0.2, lb = 10, acolor = rgb(0, 0, 1, alpha = 0.5))
arrows(8, 0.1, 11, 0.015, length = 0.1, lwd = 2)
text(8, 0.12, "13.53%", cex = 1.2)
```

Finally, the probability of a visitor spending between 2 and 6 minutes is:

`pexp(6, rate = 0.2) - pexp(2, rate = 0.2) # 0.3691258 or 36.91%`

The corresponding plot is the following:

```
exp_area(rate = 0.2, lb = 2, ub = 6, acolor = rgb(0, 0, 1, alpha = 0.5))
text(4, 0.05, "36.91%", col = "white", cex = 1.2)
```

### Plot exponential cumulative distribution function in R

You can plot the exponential cumulative distribution function passing the grid of values as first argument of the `plot`

function and the output of the `pexp`

function as the second.

```
# Grid of X-axis values
x <- seq(0, 12, 0.1)
# lambda = 2
plot(x, pexp(x, 2), type = "l",
ylab = "F(x)", lwd = 2, col = "red")
# lambda = 1
lines(x, pexp(x, rate = 1), col = "blue", lty = 1, lwd = 2)
# Adding a legend
legend("topright", c(expression(paste(, lambda)), "1", "2"),
lty = c(0, 1, 1), col = c("red", "blue"), box.lty = 0, lwd = 2)
```

Recall that `pexp(2)`

was equal to 0.8646647. In the following graph you can see the relationship between the distribution and the density function.

```
par(mfrow = c(1, 2))
#-----------------------------
# Distribution function
#-----------------------------
plot(x, pexp(x), type = "l", ylab = "F(x)", col = "blue", lwd = 2)
segments(2, 0, 2, pexp(2), lwd = 2, lty = 2)
segments(0, pexp(2), 2, pexp(2), lwd = 2, lty = 2)
#-----------------------------
# Probability density function
#-----------------------------
plot(x, dexp(x), type = "l", ylab = "f(x)", col = "red", lwd = 2)
# Area
exp_area(rate = 1, ub = 2, acolor = rgb(1, 0, 1, alpha = 0.1))
# Text
text(1, 0.1, "86.47%")
```

## The qexp function

The `qexp`

function allows you to calculate the corresponding quantile (percentile) for any probability p:

```
qexp(q,
rate = 1,
lower.tail = TRUE, # If TRUE, probabilities are P(X <= x), or P(X > x) otherwise
log.p = FALSE)
```

As an example, if you want to calculate the quantile for the probability 0.8646647 (Q(0.86)) you can type:

```
qexp(0.8646647) # 2
qexp(1 - 0.8646647, lower.tail = FALSE) # Equivalent
```

```
exp_area(rate = 1, ub = 2, acolor = rgb(0, 0, 1, alpha = 0.5))
points(2, 0, pch = 19)
arrows(4, 0.4, 2.2, 0.02, length = 0.1)
text(7.5, 0.5, "qexp(0.8646647) = 2", cex = 1.2)
```

Recall that `pexp(2)`

was equal to 0.8646647.

`lower.tail = FALSE`

if you want the probability to be on the upper tail.
### Plotting the exponential quantile function

You can make a plot of the exponential quantile function, that shows the possible outcomes of the `qexp`

function, with the code of the following block:

```
plot(qexp, pexp(0), pexp(8), lwd = 2, xlab = "p", ylab = "Q(p)")
segments(0, 2, pexp(2), 2, lty = 2, lwd = 2)
segments(pexp(2), 0, pexp(2), qexp(0.8646647), lty = 2, lwd = 2)
```

Recall that `pexp(2)`

is equal to 0.8647 and `qexp(0.8647)`

is equal to 2.

## The rexp function

The `rexp`

function allows you to draw n observations from an exponential distribution. The syntax of the function is as follows:

```
rexp(n, # Number of observations to be generated
rate = 1)
```

As an example, if you want to draw ten observations from an exponential distribution of rate 1 you can type:

`rexp(10)`

However, if you want to make the output reproducible you will need to set a seed for the R pseudorandom number generator:

```
set.seed(1)
rexp(10)
```

```
0.7551818 1.1816428 0.1457067 0.1397953 0.4360686
2.8949685 1.2295621 0.5396828 0.9565675 0.1470460
```

Observe that as you increase the number of observations, the histogram of the data approaches to the true exponential density function:

```
# Three columns
par(mfrow = c(1, 3))
x <- seq(0, 12, length = 200)
set.seed(1)
# n = 10
hist(rexp(10), main = "n = 10",
xlab = "", prob = TRUE)
lines(x, dexp(x), col = "red", lwd = 2)
# n = 100
hist(rexp(100), main = "n = 100",
xlab = "", prob = TRUE)
lines(x, dexp(x), col = "red", lwd = 2)
# n = 1000
hist(rexp(1000), main = "n = 1000",
xlab = "", prob = TRUE)
lines(x, dexp(x), col = "red", lwd = 2)
# Back to the original graphics device
par(mfrow = c(1, 1))
```