Latent Class Analysis with Dirichlet Diffusion Tree Process Prior

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Documentation for package ‘ddtlcm’ version 0.2.1

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add_leaf_branch Add a leaf branch to an existing tree tree_old
add_multichotomous_tip Add a leaf branch to an existing tree tree_old to make a multichotomus branch
add_one_sample Functions to simulate trees and node parameters from a DDT process. Add a branch to an existing tree according to the branching process of DDT
add_root Add a singular root node to an existing nonsingular tree
attach_subtree Attach a subtree to a given DDT at a randomly selected location
A_t_inv_one Compute divergence function
A_t_inv_two Compute divergence function
a_t_one Compute divergence function
a_t_one_cum Compute divergence function
a_t_two Compute divergence function
a_t_two_cum Compute divergence function
compute_IC Compute information criteria for the DDT-LCM model
create_leaf_cor_matrix Create a tree-structured covariance matrix from a given tree
data_synthetic Synthetic data example
ddtlcm_fit MH-within-Gibbs sampler to sample from the full posterior distribution of DDT-LCM
div_time Sample divergence time on an edge uv previously traversed by m(v) data points
draw_mnorm Efficiently sample multivariate normal using precision matrix from x ~ N(Q^{-1}a, Q^{-1}), where Q^{-1} is the precision matrix
expit The expit function
exp_normalize Compute normalized probabilities: exp(x_i) / sum_j exp(x_j)
H_n Harmonic series
initialize Initialize the MH-within-Gibbs algorithm for DDT-LCM
initialize_hclust Estimate an initial binary tree on latent classes using hclust()
initialize_poLCA Estimate an initial response profile from latent class model using poLCA()
initialize_randomLCM Provide a random initial response profile based on latent class mode
J_n Compute factor in the exponent of the divergence time distribution
logit The logistic function
logllk_ddt Calculate loglikelihood of a DDT, including the tree structure and node parameters
logllk_ddt_lcm Calculate loglikelihood of the DDT-LCM
logllk_div_time_one Compute loglikelihood of divergence times for a(t) = c/(1-t)
logllk_div_time_two Compute loglikelihood of divergence times for a(t) = c/(1-t)^2
logllk_lcm Calculate loglikelihood of the latent class model, conditional on tree structure
logllk_location Compute log likelihood of parameters
logllk_tree_topology Compute loglikelihood of the tree topology
log_expit Numerically accurately compute f(x) = log(x / (1/x)).
parameter_diet Parameters for the HCHS dietary recall data example
plot.ddt_lcm Create trace plots of DDT-LCM parameters
plot.summary.ddt_lcm Plot the MAP tree and class profiles of summarized DDT-LCM results
plot_tree_with_barplot Plot the MAP tree and class profiles (bar plot) of summarized DDT-LCM results
plot_tree_with_heatmap Plot the MAP tree and class profiles (heatmap) of summarized DDT-LCM results
predict.ddt_lcm Prediction of class memberships from posterior predictive distributions
predict.summary.ddt_lcm Prediction of class memberships from posterior summaries
print.ddt_lcm Print out setup of a ddt_lcm model
print.summary.ddt_lcm Print out summary of a ddt_lcm model
proposal_log_prob Calculate proposal likelihood
quiet Suppress print from cat()
random_detach_subtree Metropolis-Hasting algorithm for sampling tree topology and branch lengths from the DDT branching process.
reattach_point Attach a subtree to a given DDT at a randomly selected location
result_diet_1000iters Result of fitting DDT-LCM to a semi-synthetic data example
sample_class_assignment Sample individual class assignments Z_i, i = 1, ..., N
sample_c_one Sample divergence function parameter c for a(t) = c / (1-t) through Gibbs sampler
sample_c_two Sample divergence function parameter c for a(t) = c / (1-t)^2 through Gibbs sampler
sample_leaf_locations_pg Sample the leaf locations and Polya-Gamma auxilliary variables
sample_sigmasq Sample item group-specific variances through Gibbs sampler
sample_tree_topology Sample a new tree topology using Metropolis-Hastings through randomly detaching and re-attaching subtrees
simulate_DDT_tree Simulate a tree from a DDT process. Only the tree topology and branch lengths are simulated, without node parameters.
simulate_lcm_given_tree Simulate multivariate binary responses from a latent class model given a tree
simulate_lcm_response Simulate multivariate binary responses from a latent class model
simulate_parameter_on_tree Simulate node parameters along a given tree.
summary.ddt_lcm Summarize the output of a ddt_lcm model
WAIC Compute WAIC