simulate_DDT_tree: Simulate a tree from a DDT process. Only the tree topology...

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

Simulate a tree from a DDT process. Only the tree topology and branch lengths are simulated, without node parameters.

Description

Simulate a tree from a DDT process. Only the tree topology and branch lengths are simulated, without node parameters.

Usage

simulate_DDT_tree(K, c, c_order = 1, alpha = 0, theta = 0)

Arguments

K	number of leaves (classes) on the tree
c	hyparameter of divergence function a(t)
c_order	equals 1 (default) or 2 to choose divergence function a(t) = c/(1-t) or c/(1-t)^2.
alpha, theta	hyparameter of branching probability a(t) Gamma(m-alpha) / Gamma(m+1+theta) For DDT, alpha = theta = 0. For general multifurcating tree from a Pitman-Yor process, specify positive values to alpha and theta. It is, however, recommended using alpha = theta = 0 in inference because multifurcating trees have not been tested rigorously.

Value

A class "phylo" tree with K leaves. The leaf nodes are labeled "v1", ..., "vK", root node "u1", and internal nodes "u2", ..., "uK". Note that this tree does not contain any node parameters.

References

Knowles, D. A., & Ghahramani, Z. (2014). Pitman yor diffusion trees for bayesian hierarchical clustering. IEEE transactions on pattern analysis and machine intelligence, 37(2), 271-289.

See Also

Other simulate DDT-LCM data: simulate_lcm_given_tree(), simulate_lcm_response(), simulate_parameter_on_tree()

Examples

K <- 6
c <- 5
c_order <- 1
tree1 <- simulate_DDT_tree(K, c, c_order)
tree2 <- simulate_DDT_tree(K, c, c_order, alpha = 0.4, theta = 0.1)
tree3 <- simulate_DDT_tree(K, c, c_order, alpha = 0.8, theta = 0.1)

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