################ Code for "nicer" 2D plots  ##########################

svmPlot <- function(dt, svmModel, n=100, xname1, xname2, outcome, ...) {
  grange <- apply(dt[ ,1:2], 2, range) ## determine range for both predictors
  x1 <- seq(from=grange[1,1], to=grange[2,1], length=n)
  x2 <- seq(from=grange[1,2], to=grange[2,2], length=n)
  xgrid <- expand.grid(X1=x1, X2=x2) ## makes a grid with every 
  ## combination of X1 and X2
  names(xgrid) <- c(xname1,xname2)
  ygrid <- predict(svmModel, xgrid)
  plot(xgrid, col=c("red","blue")[as.numeric(ygrid)], pch=20, cex=0.1, ...)
  points(as.matrix(dt[dt[ ,outcome]=="-1",1:2]), col="red", pch=19, cex=0.7)
  points(as.matrix(dt[dt[ ,outcome]=="1",1:2]), col="blue", pch=19, cex=0.7)
  points(dt[svmModel$index, 1:2], pch = 5, cex = 1.5, col = "black") ## index in an svm fit
  ## gives the index of the support vectors
  beta <- drop(t(svmModel$coefs)%*% as.matrix(dt[svmModel$index, 1:2]))
  ## above line extracts beta1 and beta2 from svmModel fit -- 
  ## "%*%" means matrix multiplication
  beta0 <- svmModel$rho ## and this one gives beta0
  abline(beta0/beta[2], -beta[1]/beta[2], lwd=3)
  abline((beta0-1)/beta[2], -beta[1]/beta[2], lty=2, lwd=3, col="red")
  abline((beta0+1)/beta[2], -beta[1]/beta[2], lty=2, lwd=3, col="blue")
}