Watson distribution class

This class defines the Watson distribution. It provides methods for fitting the distribution to data and generating random samples.

Super class

midi::BaseDistribution -> WatsonDistribution

Methods

Public methods

• WatsonDistribution$new()

• WatsonDistribution$get_axis()

• WatsonDistribution$get_concentration()

• WatsonDistribution$clone()

Inherited methods

• midi::BaseDistribution$fit()
• midi::BaseDistribution$get_mean()
• midi::BaseDistribution$get_variance()
• midi::BaseDistribution$random()

---

Method new()

Creates a new Watson distribution.

Usage

WatsonDistribution$new(mu = c(0, 0, 1), kappa = 10)

Arguments

mu

A numeric vector of length 3 specifying the mean direction of the Watson distribution. Defaults to (0, 0, 1).

kappa

A numeric value specifying the concentration parameter of the Watson distribution. Defaults to 10.0.

Returns

An instance of the Watson distribution as an object of class WatsonDistribution.

---

Method get_axis()

Retrieves the mean axis of the Watson distribution.

Usage

WatsonDistribution$get_axis()

Returns

A numeric vector of length 3 storing the mean axis of the Watson distribution.

---

Method get_concentration()

Retrieves the concentration parameter of the Watson distribution.

Usage

WatsonDistribution$get_concentration()

Returns

A numeric value storing the concentration parameter of the Watson distribution.

---

Method clone()

The objects of this class are cloneable with this method.

Usage

WatsonDistribution$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

wd <- WatsonDistribution$new(
  mu = c(0, 0, 1),
  kappa = 10
)
wd$get_axis()
#> [1] 0 0 1
wd$get_concentration()
#> [1] 10
wd$random(10)
#> [[1]]
#> [1] -0.13237162  0.02374105  0.99091580
#> 
#> [[2]]
#> [1] 0.08423882 0.41784704 0.90460360
#> 
#> [[3]]
#> [1]  0.7947338 -0.0201646  0.6066230
#> 
#> [[4]]
#> [1] 0.08169297 0.07219491 0.99403931
#> 
#> [[5]]
#> [1] 0.1757129 0.1309836 0.9756886
#> 
#> [[6]]
#> [1] -0.107719805 -0.001532592  0.994180112
#> 
#> [[7]]
#> [1] -0.08345784 -0.34058075  0.93650389
#> 
#> [[8]]
#> [1]  0.02291961 -0.07663694  0.99679560
#> 
#> [[9]]
#> [1] -0.1305185  0.1437479  0.9809697
#> 
#> [[10]]
#> [1] 0.2882047 0.2054522 0.9352686
#> 
wd$fit(wd$random(100))
wd$get_axis()
#> [1]  0.03407031  0.04412166 -0.99844504
wd$get_concentration()
#> [1] 9.508437