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

• Last Updated : 23 Jun, 2020

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 t 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: My Personal Notes arrow_drop_up