R/mbd_two.R

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

mbd_two <- function(md, colnames, two=TRUE) {
  #takes output from md_bayes_draw
  # Two candidates, variables labeled the same
  # V1, V2, , when two=F, V3; 3 candidates
  # VtdHVap_cor, VtdAVap_cor, VtdBVap_cor
  if (two){
    # Hispanic
    H.num.v1.est <- md[,"ccount.VtdHVap_cor.V1"] 
    H.num.v2.est <- md[,"ccount.VtdHVap_cor.V2"]
    hispanic <- mean_and_ci(cbind(H.num.v1.est, H.num.v2.est)); 
    
    # Black Vote #
    B.num.v1.est <- md[,"ccount.VtdBVap_cor.V1"] 
    B.num.v2.est <- md[,"ccount.VtdBVap_cor.V2"]
    black <- mean_and_ci(cbind(B.num.v1.est, B.num.v2.est)); black
    
    # White #
    W.num.v1.est <- md[,"ccount.VtdAVap_cor.V1"] 
    W.num.v2.est <- md[,"ccount.VtdAVap_cor.V2"]
    white <- mean_and_ci(cbind(W.num.v1.est, W.num.v2.est)); white
    
    # Adjust output to begin to match what we have
    # Rbind the mean/CI's together
    res <- rbind(hispanic, black, white)
    # Transpose; ordering needs to be the same
    hisp2 <- t(t(c(res[1,], res[2,])))
    black2 <- t(t(c(res[3,], res[4,])))
    white2 <- t(t(c(res[5,], res[6,])))
    
    rxc <- cbind(hisp2, black2, white2) 
    rxc <- round(rxc, 5)*100 # Rounding
    row.names(rxc)[c(1,4)] <- colnames
    suppressWarnings( rxc <- data.frame(row.names(rxc), rxc) )
    colnames(rxc) <- c("Candidate","RxC: Hispanic", "RxC: Black", "RxC White")
    
    return(list(rowXcolumn = rxc, hispanic=hispanic, black=black, white=white))
  } else { # For 3-candidates, spaghetti 
    # Hispanic
    H.num.v1.est <- md[,"ccount.VtdHVap_cor.V1"] 
    H.num.v2.est <- md[,"ccount.VtdHVap_cor.V2"]
    H.num.v3.est <- md[,"ccount.VtdHVap_cor.V3"]
    hispanic <- mean_and_ci(cbind(H.num.v1.est, H.num.v2.est, H.num.v3.est)); 
    
    # Black Vote #
    B.num.v1.est <- md[,"ccount.VtdBVap_cor.V1"] 
    B.num.v2.est <- md[,"ccount.VtdBVap_cor.V2"]
    B.num.v3.est <- md[,"ccount.VtdBVap_cor.V3"]
    black <- mean_and_ci(cbind(B.num.v1.est, B.num.v2.est, B.num.v3.est)); black
    
    # White #
    W.num.v1.est <- md[,"ccount.VtdAVap_cor.V1"] 
    W.num.v2.est <- md[,"ccount.VtdAVap_cor.V2"]
    W.num.v3.est <- md[,"ccount.VtdAVap_cor.V3"]
    white <- mean_and_ci(cbind(W.num.v1.est, W.num.v2.est, W.num.v3.est)); white
    
    # Adjust output to begin to match what we have
    # Rbind the mean/CI's together
    res <- rbind(hispanic, black, white)
    
    # Transpose; ordering needs to be the same
    hisp2 <- t(t(c(res[1,], res[2,], res[3,])))
    black2 <- t(t(c(res[4,], res[5,], res[6,])))
    white2 <- t(t(c(res[7,], res[8,], res[9,])))
    
    rxc <- cbind(hisp2, black2, white2) 
    rxc <- round(rxc, 5)*100 # Rounding
    row.names(rxc)[c(1,4,7)] <- colnames
    suppressWarnings( rxc <- data.frame(row.names(rxc), rxc) )
    colnames(rxc) <- c("Candidate","RxC: Hispanic", "RxC: Black", "RxC White")
    
    return(list(rowXcolumn = rxc, hispanic=hispanic, black=black, white=white))
  }
}