small changes to chart.EFCompare and meanes.efficient.frontier

R/charts.efficient.frontier.R

@@ -842,8 +842,8 @@ chart.EfficientFrontierCompare <- function(R, portfolio, risk_type, n.portfolios
       text(out$frontier[1,m.p], out$frontier[1,2], labels = paste("(", round(out$frontier[1,m.p], 4), ",", round(out$frontier[1,2], 4), ")"), pos = 4, cex = cex.assets)
       text(out$frontier[which(x_diff == x_index),1], out$frontier[which(x_diff == x_index),2], labels = paste("(", round(out$frontier[which(x_diff == x_index),1], 4), ",", round(out$frontier[which(x_diff == x_index),2], 4), ")"), pos = 2, cex = cex.assets)
     }
-    legend.labels <- append(legend.labels, paste("% Change in Risk =", round((out$frontier[1,m.p] - out$frontier[1,1]) * 100 / out$frontier[1,1], 2)))
-    legend.labels <- append(legend.labels, paste("% Change in Return =", round((out$frontier[which(x_diff == x_index),2] - out$frontier[1,2]) * 100 / out$frontier[1,2], 2)))
+    legend.labels <- append(legend.labels, paste("% Decrease in Risk =", round((out$frontier[1,m.p] - out$frontier[1,1]) * 100 / out$frontier[1,1], 2)))
+    legend.labels <- append(legend.labels, paste("% Increase in Return =", round((out$frontier[which(x_diff == x_index),2] - out$frontier[1,2]) * 100 / out$frontier[1,2], 2)))
   }
   legend("bottomright", legend=legend.labels, col=col, lty=lty, lwd=lwd, cex=cex.legend, bty="n")
   return(invisible(out))

R/extract.efficient.frontier.R

@@ -214,7 +214,7 @@ meanvar.efficient.frontier <- function(portfolio, R, optimize_method='ROI', n.po
 #' @return a matrix of objective measure values and weights along the efficient frontier
 #' @author Ross Bennett
 #' @export
-meanetl.efficient.frontier <- function(portfolio, R, optimize_method='ROI', n.portfolios=25, ...){
+meanetl.efficient.frontier <- meanes.efficient.frontier <- function(portfolio, R, optimize_method='ROI', n.portfolios=25, ...){
   if(!is.portfolio(portfolio)) stop("portfolio object must be of class 'portfolio'")
   # step 1: find the minimum return given the constraints
   # step 2: find the maximum return given the constraints