a_t_two: Compute divergence function

In ddtlcm: Latent Class Analysis with Dirichlet Diffusion Tree Process Prior

Compute divergence function

Description

Compute value, cumulative hazard, and inverse for divergence function a(t) = c / (1-t)^2

Usage

a_t_two(c, t)

a_t_two_cum(c, t)

A_t_inv_two(c, y)

Arguments

c	a positive number for the divergence hyperparameter. A larger value implies earlier divergence on the tree
t	a number in the interval (0, 1) indicating the divergence time
y	a positive number to take inverse

Value

The value and cumulative hazard return a positive number. The inverse function returns a number in the interval (0, 1).

Functions

• a_t_two(): value of the divergence function

• a_t_two_cum(): cumulative hazard function

• A_t_inv_two(): inverse function

See Also

Other divergence functions: a_t_one()

Examples

a_t_two(1, 0.5)
a_t_two_cum(1, 0.5)
A_t_inv_two(1, 2)

ddtlcm documentation built on May 29, 2024, 5:41 a.m.