R/summarize_strand_results.R

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

#' Organize Stan output and provide summaries of model parameters
#' 
#' This is a function to organize Stan output and provide summaries of key model parameters
#'
#' @param 
#' input A STRAND model object, obtained by fitting a model using a STRAND function.
#' @param 
#' include_samples An indicator for the user to specify where raw samples, or only the summary statistics should be returned. Samples can take up a lot of space.
#' @param 
#' HPDI Highest Posterior Density Interval. Ranges in (0,1).
#' @return A STRAND results object including summary table, a summary list, and samples.
#' @export
#' @examples
#' \dontrun{
#' res = summarize_strand_results(input=fit)
#' }
#'

# Should change to allow users to specify HPDI intervals

summarize_strand_results = function(input, include_samples=TRUE, HPDI=0.9){
    if(attributes(input)$class != "STRAND Model Object"){
        stop("summarize_strand_results() requires a fitted object of class: STRAND Model Object.")
    }

    if(attributes(input)$fit_type != "mcmc"){
        stop("Fitted results can only be reorganized for STRAND model objects fit using MCMC. Variational inference or optimization can be used in Stan
              during experimental model runs, but final inferences should be based on MCMC sampling.")   
    }

    if(attributes(input)$model_type == "SRM"){
       res = summarize_srm_results(input=input, include_samples=include_samples, HPDI=HPDI)
      }

    if(attributes(input)$model_type == "SBM"){
       res = summarize_bm_results(input=input, include_samples=include_samples, HPDI=HPDI)
      }

    if(attributes(input)$model_type == "SRM+SBM"){
       res = summarize_bsrm_results(input=input, include_samples=include_samples, HPDI=HPDI)
      }

    if(attributes(input)$model_type == "SRM+SBM+ME"){
       res = summarize_bsrm_results_with_measurement_bias(input=input, include_samples=include_samples, HPDI=HPDI)
      }

    if(attributes(input)$model_type == "LNM"){
       res = summarize_lnm_results(input=input, include_samples=include_samples, HPDI=HPDI)
      }

    if(attributes(input)$model_type == "LNM+Flows"){
       res = summarize_lnmf_results(input=input, include_samples=include_samples, HPDI=HPDI)
      }

    if(attributes(input)$model_type == "Multiplex"){
       res = summarize_multiplex_bsrm_results(input=input, include_samples=include_samples, HPDI=HPDI)
      }

    return(res)
}