summary.ddt_lcm {ddtlcm}

Summarize the output of a ddt_lcm model

Description

Summarize the output of a ddt_lcm model

Usage

## S3 method for class 'ddt_lcm'
summary(object, burnin = 3000, relabel = TRUE, be_quiet = FALSE, ...)

Arguments

object

a "ddt_lcm" object

burnin

number of samples to discard from the posterior chain as burn-ins. Default is 3000.

relabel

If TRUE, perform post-hoc label switching using the Equivalence Classes Representatives (ECR) method to solve non-identifiability issue in mixture models. If FALSE, no label switching algorithm will be performed.

be_quiet

If TRUE, do not print information during summarization. If FALSE, print label switching information and model summary.

...

Further arguments passed to each method

Value

an object of class "summary.ddt_lcm"; a list containing the following elements:

tree_map

the MAP tree of "phylo4d" class

tree_Sigma

the tree-structured covariance matrix associated with tree_map

response_probs_summary, class_probs_summary, Sigma_summary, c_summary

each is a matrix with 7 columns of summary statistics of posterior chains, including means, standard deviation, and five quantiles. In particular, for the summary of item response probabilities, each row name theta_k,g,j represents the response probability of a person in class k to consume item j in group g

max_llk_full

a numeric value of the maximum log-likelihood of the full model (tree and LCM)

max_llk_lcm

a numeric value of the maximum log-likelihood of the LCM only

Z_samples

a N x total_iters integer matrix of posterior samples of individual class assignments

Sigma_by_group_samples

a G x total_iters matrix of posterior samples of diffusion variances

c_samples

a total_iters vector of posterior samples of divergence function hyperparameter

loglikelihood

a total_iters vector of log-likelihoods of the full model

loglikelihood_lcm

a total_iters vector of log-likelihoods of the LCM model only

setting

a list of model setup information. See ddtlcm_fit

controls

a list of model controls. See ddtlcm_fit

data

the input data matrix

See Also

Other ddt_lcm results: print.ddt_lcm(), print.summary.ddt_lcm()

Examples

# load the result of fitting semi-synthetic data with 1000 (for the sake of time) posterior samples
data(result_diet_1000iters)
summarized_result <- summary(result_diet_1000iters, burnin = 500, relabel = TRUE, be_quiet = TRUE)