Compute Beta Distribution in R Programming - dbeta(), pbeta(), qbeta(), and rbeta() Functions

Beta Distribution in R Language is defined as property which represents the possible values of probability. This article is an illustration of dbeta, pbeta, qbeta, and rbeta functions of Beta Distribution.

dbeta() Function

It is defined as Beta Density function and is used to create beta density value corresponding to the vector of quantiles.

Syntax: dbeta(vec, shape1, shape2) Parameter: vec: Vector to be used shape1, shape2: beta density of input values Returns: beta density values for a vector of quantiles

Example : 

# R program to illustrate the use of 
# dbeta() function

# Creating a vector
x_beta <- seq(0, 1.5, by = 0.025 )  

# Apply beta function          
y_beta <- dbeta(x_beta, shape1 = 2, shape2 = 4.5)  

# Plot beta values
plot(y_beta)                                       

Output:

pbeta() Function

It is used to create cumulative distribution function of the beta distribution.

Syntax: pbeta(vec, shape1, shape2) Parameter: vec: Vector to be used shape1, shape2: beta density of input values

Example: 

# Specify x-values for pbeta function
x_pbeta <- seq(0, 1, by = 0.025)      

# Apply pbeta() function
y_pbeta <- pbeta(x_pbeta, shape1 = 1, shape2 = 4)  

# Plot pbeta values
plot(y_pbeta)

Output:

qbeta() Function

It is known as beta quantile function and used to return quantile values of the function.

Syntax: qbeta(vec, shape1, shape2) Parameters: vec: Vector to be used shape1, shape2: beta density of input values

Example: 

  
# Specify x-values for qbeta() function
x_qbeta <- seq(0, 1, by = 0.025)

# Apply qbeta() function
y_qbeta <- qbeta(x_qbeta, shape1 = 1, shape2 = 4)  

# Plot qbeta() values
plot(y_qbeta) 

Output:

rbeta() Function

It is defined as a random number generator that is used to set seed and specify sample size.

Syntax: rbeta(N, shape1, shape2 ) Parameters: vec: Vector to be used shape1, shape2: beta density of input values

Example: 

# Set seed for reproducibility
set.seed(13579)

# Specify sample size
N <- 10000  

# Draw N beta distributed values
y_rbeta <- rbeta(N, shape1 = 1, shape2 = 5)   
y_rbeta

# Plot of randomly drawn beta density
plot(density(y_rbeta), 
     main = "beta Distribution in R")

Output: