R/desc.wts.fast.R

In twang: Toolkit for Weighting and Analysis of Nonequivalent Groups

desc.wts.fast <-function(data,w, sampw = sampw, 
                    vars=NULL,
                    treat.var,
                    tp,
                    na.action="level",
                    perm.test.iters=0,
                    verbose=TRUE,
                    alerts.stack,
                    estimand, multinom = FALSE, fillNAs = FALSE,
                    ks.exact=NULL){
  
  if(is.null(vars)) vars <- names(data)[names(data)!=treat.var]
  
  ## calculate effective sample sizes
  ess.ctrl          <- (sum(w[data[,treat.var]==0])^2)/sum(w[data[,treat.var]==0]^2)
  ess.treat    <- (sum(w[data[,treat.var]==1])^2)/sum(w[data[,treat.var]==1]^2)
  
  sampW <- sampw
  vars1 <- vars
  treat.var1 <- treat.var
  
  bal.tab   <- bal.stat.fast(data=data,w.all=w, sampw = sampW,
                        vars=vars1,
                        treat.var=treat.var1,
                        na.action=na.action,
                        estimand=estimand, multinom = multinom, fillNAs = fillNAs,
                        ks.exact=ks.exact)
  pval.maxks <- NA
  # compute permutation p-values for KS statistic
  if(perm.test.iters>0)
  {
    ess.t <- (sum(w[data[,treat.var]==1])^2)/
      sum(w[data[,treat.var]==1]^2)
    ess.c <- (sum(w[data[,treat.var]==0])^2)/
      sum(w[data[,treat.var]==0]^2)
    w.1   <- w
    
    ### revised 091710
    w.1[data[,treat.var]==0] <- ess.c*w[data[,treat.var]==0]/sum(w[data[,treat.var]==0])
    w.1[data[,treat.var]==1] <- ess.t*w[data[,treat.var]==1]/sum(w[data[,treat.var]==1])
    w.1 <- w.1/(ess.c+ess.t)
    
    pval.var   <- rep(0,nrow(bal.tab$results))
    names(pval.var) <- rownames(bal.tab$results)
    pval.maxks <- 0
    if(verbose)
    {
      cat("Permutation test progress:\n")
      progress <- round(perm.test.iters*c(0.01,(1:10)/10))
    }
    for(i.rep in 1:perm.test.iters)
    {
      if(verbose && (i.rep %in% progress))
      {
        cat(round(100*i.rep/perm.test.iters),"%\n",sep="")
      }
      i <- sample(1:nrow(data),ess.t+ess.c,replace=TRUE,prob=w.1)
      temp <- data[i,c(vars,treat.var)]
      temp[,treat.var] <- as.numeric((1:nrow(temp))<=ess.t)
      bal.temp   <- bal.stat(data=temp,
                             w.all=rep(1,length(i)),
                             sampw = rep(1,length(i)),
                             vars=vars1,
                             treat.var=treat.var1,
                             na.action=na.action,
                             get.means=FALSE,
                             estimand=estimand, multinom = multinom)
      # if a bootstrap sample is missing a level need to make sure
      #   these still align
      j <- match(rownames(bal.temp$results),names(pval.var))
      pval.var[j] <- pval.var[j]   + 
        (bal.temp$results$ks >= bal.tab$results$ks[j])
      pval.maxks  <- pval.maxks + 
        (max(bal.temp$results$ks) >= max(bal.tab$results$ks))
    }
    pval.var   <- as.numeric(pval.var/perm.test.iters)
    pval.maxks <- as.numeric(pval.maxks/perm.test.iters)
    
    # replace the analytic p-values computed in bal.tab
    bal.tab$results$ks.pval <- pval.var
  }
  
  check.err(cov.table=bal.tab$results, stage=tp, alerts.stack=alerts.stack, estimand=estimand, ess.ctrl=ess.ctrl, ess.treat=ess.treat)
  
  max.ks  <- max(bal.tab$results$ks)
  mean.ks  <- mean(bal.tab$results$ks)
  max.es <- with(bal.tab$results, max(abs(std.eff.sz[std.eff.sz<500]),
                                      na.rm=TRUE))
  mean.es   <- with(bal.tab$results, mean(abs(std.eff.sz[std.eff.sz<500]),
                                          na.rm=TRUE))
  return(list(ess.ctrl=ess.ctrl,
              ess.treat=ess.treat,
              n.treat=sum(data[,treat.var]==1),
              n.ctrl =sum(data[,treat.var]==0),
              max.es=max.es,
              mean.es=mean.es,
              max.ks=max.ks,
              max.ks.p=pval.maxks,
              mean.ks=mean.ks,
              bal.tab=bal.tab))
}