R/generate_structured_correlation_matrix.R

In ctross/STRAND: An R package for simulating and analyzing social network data in R and Stan

#' A function to generate correlation matrices with special inner symmetry
#'
#' This is an internal function.
#'
#' @param data A data list.
#' @param eta Prior on the LKJ Cholesky factor.
#' @param mode Which method to use? "cholesky" or "l2norm".
#' @param setting Which matrix to create? "multiplex_dyadic_reciprocity", "longitudinal_dyadic_reciprocity", or "longitudinal_generalized_reciprocity".
#' @param stan_mcmc_parameters A list of Stan parameters that often need to be tuned. 
#' @return A Stan object.
#' @export
#'

generate_structured_correlation_matrix = function(data, eta, mode, setting, stan_mcmc_parameters){
  data$eta = eta

  if(setting=="multiplex_dyadic_reciprocity"){
   data$setting = 1
   data = build_multiplex_bindings_dr_multiplex(data)
   }

  if(setting=="longitudinal_dyadic_reciprocity"){
   data$setting = 2
   data = build_multiplex_bindings_dr_longitudinal(data)
   }

  if(setting=="longitudinal_generalized_reciprocity"){
   data$setting = 3
   data = build_multiplex_bindings_sr_longitudinal(data)
   
   # Have to do a little renaming, so that the Stan file works for both dyadic and generalized matrices
   data$N_dr_bindings = data$N_sr_bindings 
   data$N_dr_params = data$N_sr_params
   data$N_dr_indices = data$N_sr_indices
   data$dr_indices = data$sr_indices
   data$dr_id = data$sr_id
  }


  if(mode=="cholesky"){
    model = cmdstanr::cmdstan_model("Code/Stan/generate_structured_correlation_matrix_cholesky.stan")
  }

  if(mode=="l2norm"){
    model = cmdstanr::cmdstan_model("Code/Stan/generate_structured_correlation_matrix_l2norm.stan")
  }
  
  fit = model$sample(
        data = unclass(data),
        seed = stan_mcmc_parameters$seed,
        chains = stan_mcmc_parameters$chain,
        parallel_chains = stan_mcmc_parameters$parallel_chains,
        refresh = stan_mcmc_parameters$refresh,
        iter_warmup = stan_mcmc_parameters$iter_warmup,
        iter_sampling = stan_mcmc_parameters$iter_sampling,
        max_treedepth = stan_mcmc_parameters$max_treedepth,
        adapt_delta = stan_mcmc_parameters$adapt_delta,
        init = stan_mcmc_parameters$init
        )
       
    bob = list(data=data, fit=fit)  
    return(bob)
 }