R/lambda_two_compare.R

In lorenc5/eiCompare: Compares EI, Goodman, RxC Estimates

lambda_two_compare <- function(lmd, cnames, group_name="Latino", 
                               cand1or2=1) {
  
  if (cand1or2==2){
    
    cnames <- rev(cnames)
    p.cand_over_cand<-lmd[,cnames[2]]-lmd[,cnames[1]]
    
  }   
  
  n <- nrow(lmd)
  labels <- sub('.*\\.', '', cnames)
  
  #what is probability that Morales is preferred to Rothman among Latinos?
  p.cand_over_cand<-lmd[,cnames[1]]-lmd[,cnames[2]]
  
  # Plot Histogram & Posterior #
  par(mfrow=c(1,2))
  
  # Histogram #
  hist (lmd[,cnames[1]] - lmd[,cnames[2]], 
        main = paste(labels[1], " - ",labels[2], sep=""),
        xlab = "Posterior Distribution")
  
  # Density #
  
  # Need regular expression for everything to right of period to extract names #
  dens=density(p.cand_over_cand)
  plot(dens,main=paste("Difference in Proportion ", group_name, 
                       "\nVote for ", labels[1], " and ", labels[2], sep=""),
       xlab="Difference in Posterior Distribution Sampled from ")
  points(p.cand_over_cand,rep(0,length(p.cand_over_cand)),pch=3) #plot individual posterior sampled points (no)
  abline(v=0, col="grey", lty=2)
  
  # Probability 1 Candidate over Another #
  c1g10 <- length(which(p.cand_over_cand>.10))/n # Morales over Rothman by at least 10 points
  c1g5 <- length(which(p.cand_over_cand>.05))/n # # Morales over Rothman by at least 5 points
  c1g0 <- length(which(p.cand_over_cand>0))/n
  
  med <- median(p.cand_over_cand)
  mean <- mean(p.cand_over_cand)
  df <- data.frame(c1g10, c1g5, c1g0, round(med,3), round(mean, 3))
  colnames(df) <- c("Prob>10%", "Prob>5%", "Prob>0", "Dist. Median", "Dist. Mean")
  return( df )
  
}