numerics_summary_do_not_need_alignment {DGP4LCF}

Numerical summary for important continuous variables that do not need alignment.

Description

Numerical summary for important continuous variables that do not need alignment.

Usage

numerics_summary_do_not_need_alignment(
  burnin = 0,
  thin_step = 1,
  pred_x_truth_indicator = FALSE,
  pred_x_truth = NULL,
  gibbs_after_mcem_combine_chains_result
)

Arguments

burnin

A numeric scalar. The saved samples are already after burnin; therefore the default value for this parameter here is 0. Can discard further samples if needed.

thin_step

A numeric scalar. The saved samples are already after thinning; therefore the default value for this parameter here is 1. Can be further thinned if needed.

pred_x_truth_indicator

A logical value. pred_x_truth_indicator = TRUE means that truth of predicted gene expressions are available. The default value is FALSE.

pred_x_truth

Only needed if pred_x_truth_inidcator = TRUE. An array of dimension (n, p, num_time_test), storing true gene expressions in the testing data.

gibbs_after_mcem_combine_chains_result

A list of objects returned from the function 'gibbs_after_mcem_combine_chains'.

Details

This function corresponds to Algorithm 2: Steps 3 and 4 in the main manuscript; therefore reader can consult the paper for more explanations.

Value

Convergence assessment for important continuous variables that do not need alignment, and posterior summary for predicted gene expressions.

Examples

# See examples in vignette
vignette("bsfadgp_regular_data_example",  package = "DGP4LCF")