R/MEMS_QAP.R

In netmediate: Micro-Macro Analysis for Social Networks

#
# This function estimates the MEMS using ERGM with nonparametric algorithm
#
#
# model is the ERGM object
#micro_process is the micro process of interest, provided as a character string
#macro_function is the function to be calculated on the network object. NOTE: currently only supports statistics calculated on network and igraph objects.
#object type is the type of object used in the function to calculate macro statistics. Currently only supports igraph and network objects. If left NULL, the object is assumed to be a network object.
#the interval provides the values over which to calculate the MEMS. It defaults to 0 and 1
#nsim is the number of bootstrap resamples
# silent tells R whether to provide updates on the algorithm's progress
#full_output tells R whether to return simulated distribution in addition to summary statistic



MEMS_QAP <- function(model,
                     micro_process,
                     macro_function,
                     object_type=object_type,
                     interval=interval,
                     nsim=nsim,
                     silent=silent,
                     full_output=full_output,
                     net_logit_y=net_logit_y,
                     net_logit_x=net_logit_x,
                     mediator=mediator,
                     link_id=link_id,
                     controls=controls,
                     control_functions=control_functions) {



  interval<-sort(interval) #order from lowest to highest


  message("Computing MEMS over ",interval[1],"-",interval[length(interval)]," interval.")
  message("Getting bootstrap parameter vector.")

  #start bootstrap with pseudolikelihood
  theta<-matrix(NA,nrow=nsim,ncol=length(coef(model)))
  colnames(theta)<-model$names

  for(j in 1:nsim){

    bs_y<-net_logit_y[sample(nrow(net_logit_y),replace=TRUE)]
    bs_x<-net_logit_x[sample(nrow(net_logit_x),replace=TRUE),,]
    bs_model<-sna::netlogit(net_logit_y,net_logit_x,reps=100) #set to 100 to reduce computational burden

    theta[j,]<-coef(bs_model)


    if(silent==FALSE){

      message("Getting bootstrap parameters ", j, " of ", nsim, " complete.")

    }

  }

  vector_data<-as.data.frame(sna::gvectorize(net_logit_x,diag=TRUE))
  vector_data<-cbind.data.frame(rep(1,nrow(vector_data)),vector_data)
  colnames(vector_data)<-colnames(theta)

  #create list of values to predict over
  mat_list<-list()
  for(i in 1:length(interval)){
    mat_list[[i]]<-vector_data

  }

  #create lists of data for each link_id and control. These lists provide the data for AMME function
  if(!is.null(link_id)){

    link_list_data<-list()
    for(i in 1:nsim){
      link_list_data[[i]]<-matrix(NA,nrow=length(unique(link_id[[1]])),ncol=length(interval))
      rownames(link_list_data[[i]])<-unique(link_id[[1]])
    }

    if(length(controls)>0){
      controls_list<-as.list(controls)
      for(i in 1:length(controls)){
        sim_list<-list()
        for(j in 1:nsim){
          sim_list[[j]]<-matrix(NA,nrow=length(unique(link_id[[i+1]])),ncol=length(interval))
          rownames(sim_list[[j]])<-unique(link_id[[i+1]])
        }
        controls_list[[i]]<-sim_list
        names(controls_list)<-controls

      }
    }else{
      controls_list<-NULL
    }
  }


  output_data<-matrix(NA,nrow=nsim,ncol=length(interval))
  aMEMS_tracer<-0

  #main loop
  for(j in 1:nrow(theta)){

    #create networks for k intervals

    net_list<-list()
    for(i in 1:length(mat_list)){

      pred_mat<-mat_list[[i]]
      cbcoef<-theta[j,]
      cbcoef[micro_process]<-cbcoef[micro_process]*interval[i]

      #predict ties
      lp <- apply(pred_mat, 1, function(x) t(x) %*% cbcoef)
      result <- c(1/(1 + exp(-lp)))

      #create network, assign vertex attributes, by creating empty network and adding new edges
      pred_y <- network::as.network(sna::rgraph(nrow(net_logit_y),tprob=result))      # create predicted ties

      ##assign attributes
      for(column in 1:ncol(vector_data)){

        network::set.edge.attribute(pred_y,colnames(vector_data)[column],vector_data[,column])

      }
      net_list[[i]]<-pred_y
    }


      if(object_type%in%c("network")){

        for(i in 1:length(net_list)){
          b<-macro_function(net_list[[i]])
          if(!is.null(link_id)){
            link_list_data[[j]][,i]<-b
          }
          if(length(b)>1){
            b<-mean(b,na.rm=TRUE)
            aMEMS_tracer<-1} ##handle node and multiple obs characteristics
          output_data[j,i]<-b
        }

      }else{
        #for igraph functions
        for(i in 1:length(net_list)){
          net_list[[i]]<-intergraph::asIgraph(net_list[[i]])
          b<-macro_function(net_list[[i]])
          if(!is.null(link_id)){
            link_list_data[[j]][,i]<-b
          } #
          if(length(b)>1){
            b<-mean(b,na.rm=TRUE)
            aMEMS_tracer<-1} ##handle node and multiple obs characteristics
          output_data[j,i]<-b
          if(length(controls)>0){
            net_list[[i]]<-intergraph::asNetwork(net_list[[i]]) # convert back to network for control operations
          }

        }

      }

    if(length(controls)>0){

      for(control in 1:length(controls_list)){
      if(object_type[control+1]%in%c("network")){

        for(i in 1:length(net_list)){
          b<-control_functions[[control]](net_list[[i]])
          controls_list[[control]][[j]][,i]<-b
        }

      }else{
        #for igraph functions
        for(i in 1:length(net_list)){
          net_list[[i]]<-intergraph::asIgraph(net_list[[i]])
          b<-control_functions[[control]](net_list[[i]])
          controls_list[[control]][[j]][,i]<-b
          net_list[[i]]<-intergraph::asNetwork(net_list[[i]]) #convert back to network object for further loops

        }
      }
    }#close if controls statement

    }#close controls loop

    if(silent==FALSE){
      print(paste("Simulation ",j," out of", nsim," complete"))
    }



  }#close bootstrap loop


  ###calculate summary statistics

  if(aMEMS_tracer==1 &is.null(link_id)){
    message("More than one macro statistic is being calculated. Reporting the aMEMS.")
  }


  diff_data<-matrix(NA,nrow=nrow(output_data),ncol=ncol(output_data)-1)

  for(i in 1:ncol(diff_data)){
    k<-i+1
    diff_data[,i]<-output_data[,k]-output_data[,i]
  }

  summary_dat<-matrix(NA,nrow=2,ncol=5)
  rownames(summary_dat)<-c("(a)MEMS","Prop. Change in M")
  colnames(summary_dat)<-c("Estimate","Std. Dev.","lower 95% CI","Upper 95% CI","BS p-val")

  summary_dat[1,1]<-mean(diff_data,na.rm=TRUE)
  summary_dat[1,2]<-sd(diff_data,na.rm=TRUE)

  if(summary_dat[1,1]<0){
    summary_dat[1,5]<-length(diff_data[which(diff_data>=0)])/nsim
  }else{
    summary_dat[1,5]<-length(diff_data[which(diff_data<=0)])/nsim

  }
  summary_dat[1,3]<-quantile(diff_data,.025,na.rm=TRUE)
  summary_dat[1,4]<-quantile(diff_data,.975,na.rm=TRUE)



  prop_change<-matrix(NA,nrow=nrow(output_data),ncol=ncol(output_data)-1)

  for(i in 1:ncol(prop_change)){
    k<-i+1
    prop_change[,i]<-(diff_data[,i]/output_data[,k])
  }

  prop_change<-prop_change[!is.infinite(prop_change)]

  summary_dat[2,1]<-mean(prop_change,na.rm=TRUE)

  if(full_output==FALSE){
    return(summary_dat)
  }else{
    out_dat<-list(summary_dat=summary_dat,
                  output_data=output_data,
                  mems_samples=diff_data)
    if(is.null(link_id)){

      return(out_dat) #return data no controls

    }else{

      #return data for AMME call
      out_dat<-list(out_dat_main=link_list_data,
                    out_dat_controls=controls_list)

      return(out_dat)


    }
  }

}