ddtlcm (version 0.2.1)

initialize_hclust: Estimate an initial binary tree on latent classes using hclust()

Description

Estimate an initial binary tree on latent classes using hclust()

Usage

initialize_hclust(
  leaf_data,
  c,
  c_order = 1,
  method_dist = "euclidean",
  method_hclust = "ward.D",
  method_add_root = "min_cor",
  alpha = 0,
  theta = 0,
  ...
)

Value

phylo4d object of tree topology

Arguments

leaf_data

a K by J matrix of \(logit(theta_{kj})\)

c

hyparameter of divergence function a(t)

c_order

equals 1 (default) or 2 to choose divergence function

method_dist

string specifying the distance measure to be used in dist(). This must be one of "euclidean", "maximum", "manhattan", "canberra", "binary" or "minkowski". Any unambiguous substring can be given.

method_hclust

string specifying the distance measure to be used in hclust(). This should be (an unambiguous abbreviation of) one of "ward.D", "ward.D2", "single", "complete", "average" (= UPGMA), "mcquitty" (= WPGMA), "median" (= WPGMC) or "centroid" (= UPGMC).

method_add_root

string specifying the method to add the initial branch to the tree output from hclust(). This should be one of "min_cor" (the absolute value of the minimum between-class correlation) or "sample_ddt" (randomly sample a small divergence time from the DDT process with a large c = 100)

alpha, theta

hyparameter of branching probability a(t) Gamma(m-alpha) / Gamma(m+1+theta) For DDT, alpha = theta = 0

...

optional arguments for the poLCA function

See Also

Other initialization functions: initialize(), initialize_poLCA()