R/gile-ss-util.R

Defines functions getinclCstacked getestCstacked probtodist

# turn sampling probabilities and sample counts into a synthetic population
probtodist<-function(classes,nums,prob,n,hajek=TRUE){
  #classes: size value of each class 
  #nums: number of members of that class in the sample
  #prob: Probabilities of selection for a member of that class
  props<-rep(0,length(classes))
  for(k in 1:length(classes)){
    props[k]<-nums[k]/prob[k]
  }
  # Hajek is the one in Krista's thesis and it implicitly multiplies the
  # HT estimate of the proportions by N as an estimate of the population size.
  # Otherwise use the HT estimator which implicitly multiples by the HT
  # estimate of the population size)
  #
  # The Hajek estimator is significantly less efficient than the HT estimator
  # under PPSWOR when the outcome is approximately proportional to degree.
  # When the disease status is unrelated to the degree the Hajek is slightly
  # more efficient (all assuming that the population size is actually N)
  #
  # In this case we are estimating the counts in each class not the disease
  # outcome
  #
  if(!hajek){
    n<-round(sum(props))
  }
  props<-props/sum(props)
  if(any(is.na(props))){
    print("Error in proptodist")
  }	
  #classes: size value of each class: 
  #props: proportion of the population in that class
  list(classes=classes, props=props, n=n)	
}




getestCstacked<-function(samp,n,nit=5, nsampsamp=1000, trace=FALSE, hajek=TRUE,SS.infinity=0.04){
  # samp: sample
  # n: population size
  classes<-sort(unique(samp))
  nums<-table(samp)
  nsamp<-length(samp)
  prob<-classes/sum(samp)
  prob <- prob * sum(nums/prob) / n
  
  #temp$classes: size value of each class 
  #temp$nums: number of members of that class 
  #temp$prob: Probabilities of selection for a member of that class
  temp<-probtodist(classes=classes,nums=nums,prob=prob,n=n,hajek=hajek)
  #temp$classes: size value of each class 
  #temp$props: proportion of the population in that class
  if(n*SS.infinity < nsamp){
    newprobs<-getinclCstacked(classes=temp$classes,props=temp$props,n=n,
                              nsamp=nsamp,
                              nsampsamp=nsampsamp,
                              nums=nums)
    #newprobs$degvec: size value of each class
    #newprobs$pvec: Probabilities of selection for a member of that class
    
    for(i in 1:nit){
      if(!hajek){
        cat(sprintf("The estimated population size is %d.\n",temp$n))
      }
      temp<-probtodist(classes=newprobs$degvec,nums=nums,prob=newprobs$pvec,
                       n=temp$n,hajek=hajek)
      newprobs<-getinclCstacked(classes=temp$classes,props=temp$props,
                                n=temp$n,
                                nsamp=nsamp,
                                nsampsamp=nsampsamp,
                                nums=nums)
    }
  }else{
    if(nsamp < n){
      #    VH
      pvec=nsamp*(temp$classes)/(temp$n*sum(temp$props*temp$classes))
    }else{
      #    Arithmetic Mean
      pvec=rep(nsamp,length=temp$classes)/temp$n
    }
    newprobs <- list(degvec=temp$classes, pvec=pvec,
                     nbyclass=round(temp$n*temp$props/sum(temp$props)))
  }
  temp<-probtodist(classes=newprobs$degvec,nums=nums,prob=newprobs$pvec,
                   n=temp$n,hajek=hajek)
  if(!hajek){
    cat(sprintf("The estimated population size is %d.\n",temp$n))
  }
  #classes: size value of each class 
  #props: proportion of the population in that class
  #probs: Probabilities of selection for a member of that class
  list(probs=newprobs$pvec,props=temp$props,classes=temp$classes,n=temp$n)
}


getinclCstacked<-function(classes,props,n,nsamp,nsampsamp,nums,hajek=TRUE){
  props2<-props/sum(props)
  #artificially inflate each value to at least 1 node
  nbyclass<-round(n*props2)
  nbyclass[nbyclass==0]<-1
  offby<-n-sum(nbyclass)
  if(is.na(offby)){
    print("Error in getincl: offby")
  }
  # based on estimate of degrees, estimate true inclusion probs by class
  Cret <- .C("getinclCstacked",
             nbyclass=as.integer(nbyclass),
             size=as.double(classes),
             props2=as.double(props2),
             offby=as.integer(offby),
             N=as.integer(n),
             K=as.integer(length(classes)),
             n=as.integer(nsamp),
             samplesize=as.integer(nsampsamp),
             Nk=as.integer(classes),
             PACKAGE="RDS")
  Ninf <- (Cret$Nk+nums)/(sum(Cret$Nk)+nsamp)
  Ninf <- nsamp*Ninf/Cret$nbyclass
  list(degvec=classes,pvec=Ninf,nbyclass=Cret$nbyclass)
}

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RDS documentation built on Dec. 2, 2017, 1:08 a.m.