ROOPSD (version 0.3.9)

GEV: GEV

Description

GEV distribution in OOP way. Based on AbstractDist

Super class

ROOPSD::AbstractDist -> GEV

Active bindings

loc

[double] location of the GEV law

scale

[double] scale of the GEV law

shape

[double] shape of the GEV law

params

[vector] params of the GEV law

Methods

Public methods

• GEV$new()

• GEV$qgradient()

• GEV$pgradient()

• GEV$clone()

Inherited methods

• ROOPSD::AbstractDist$cdf()
• ROOPSD::AbstractDist$density()
• ROOPSD::AbstractDist$diagnostic()
• ROOPSD::AbstractDist$fit()
• ROOPSD::AbstractDist$icdf()
• ROOPSD::AbstractDist$isf()
• ROOPSD::AbstractDist$logdensity()
• ROOPSD::AbstractDist$pdeltaCI()
• ROOPSD::AbstractDist$qdeltaCI()
• ROOPSD::AbstractDist$rvs()
• ROOPSD::AbstractDist$sf()

---

Method new()

Create a new GEV object.

Usage

GEV$new(loc = 0, scale = 1, shape = -0.1)

Arguments

loc

[double] location parameter

scale

[double] scale parameter

shape

[double] shape parameter

Returns

A new `GEV` object.

---

Method qgradient()

Gradient of the quantile function

Usage

GEV$qgradient(p, lower.tail = TRUE)

Arguments

p

[vector] Probabilities

lower.tail

[bool] If CDF or SF.

Returns

[vector] gradient

---

Method pgradient()

Gradient of the CDF function

Usage

GEV$pgradient(x, lower.tail = TRUE)

Arguments

x

[vector] Quantiles

lower.tail

[bool] If CDF or SF.

Returns

[vector] gradient

---

Method clone()

The objects of this class are cloneable with this method.

Usage

GEV$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Details

See AbstractDist for generic methods

Examples

## Generate sample
loc   = 0
scale = 0.5
shape = -0.3
gev = ROOPSD::GEV$new( loc = loc , scale = scale , shape = shape )
X   = gev$rvs( n = 1000 )

## And fit parameters
gev$fit(X)