R/logL_entry.R

In FedIRT: Federated Item Response Theory Models

#' @title Aggregate Log-Likelihood Function for Federated Learning
#'
#' @description Computes the sum of log-likelihoods across multiple schools in a federated learning setting. The function `logL_entry` aggregates contribution of each school's log-likelihood to the overall model. It is designed to be used within the optimization process of `fedirt`.
#'
#' @details In a federated learning context, each school computes its log-likelihood locally. The `logL_entry` function is responsible for aggregating these values. Users are expected to provide an implementation for `getlogL_from_index`, which should include network requests to retrieve log-likelihoods calculated by each school, or for simplified prototyping purposes, could directly use a `logL` function to compute likelihoods locally.
#'
#' @param ps A parameter vector consisting of item parameters; it should include both discrimination (a) and difficulty (b) parameters.
#'
#' @return The sum of log-likelihoods as a single numeric value, representing the likelihood of the entire federated dataset under the current model's parameters.
#'

#' @export

logL_entry = function(ps) {
  # a = matrix(ps[1:J])
  # b = matrix(ps[(J+1):(2*J)])
  # print(paste0("logL_entry::", J))
  if(K==1){
    result = getlogL_from_index(ps,1)
  } else{
    result = 0
    for(index in 1:K) {
      result = result + as.numeric(getlogL_from_index(ps,index))
    }
  }

  # print(result)
  result
}