update plotFrontier for EQS

Author: xinran1228

R/plotFrontiers.R

@@ -14,25 +14,28 @@
 #' 
 #' @details
 #' This function provides the ability to plot frontiers based on the result of 
-#' `meanvar.efficient.frontier`, `meanetl.efficient.frontier` or `meancsm.efficient.frontier`.
+#' `meanvar.efficient.frontier`, `meanetl.efficient.frontier`, `meancsm.efficient.frontier`
+#' or `meaneqs.efficient.frontier`.
 #' 
-#' When using \code{meanvar.efficient.frontier}, \code{meanetl.efficient.frontier} 
-#' and \code{meancsm.efficient.frontier}, the result will be frontiers data,
-#' including the weights for each point on the mean-risk efficient frontiers. 
-#' Before using this function, user should declare which risk that they want to 
-#' compare, and what parameters that they want to use to calculate the risk, 
-#' e.g. \code{ES_alpha} for ES, \code{moment_setting} for var. Then this function 
-#' will calculate back mean and risk based on the weight, and draw a plot.
+#' When using \code{meanvar.efficient.frontier}, \code{meanetl.efficient.frontier}, 
+#' \code{meancsm.efficient.frontier} and \code{meaneqs.efficient.frontier}, the 
+#' result will be frontiers data, including the weights for each point on the 
+#' mean-risk efficient frontiers. Before using this function, user should declare 
+#' which risk that they want to compare, and what parameters that they want to 
+#' use to calculate the risk, e.g. \code{ES_alpha} for ES, \code{moment_setting} 
+#' for var. Then this function will calculate back mean and risk based on the 
+#' weight, and draw a plot.
 #' 
 #' Default settings use colors and line types to differentiate portfolios, and set 
 #' the portfolio name as 'Portfolio 1' and so on. Users could customize col, lty, 
 #' lwd and legend.labels to better the plot.
 #' 
 #' @param R an xts object of asset returns
 #' @param frontiers a list of frontiers, for example, list(ef1=meanvar.efficient.frontier(), ef2=meanvar.efficient.frontier())
-#' @param risk type of risk that you want to compare, could be 'StdDev', 'ES', 'CSM'
+#' @param risk type of risk that you want to compare, could be 'StdDev', 'ES', 'CSM', 'EQS'
 #' @param ES_alpha the default value is 0.05, but could be specified as any value between 0 and 1
 #' @param CSM_alpha the default value is 0.05, but could be specified as any value between 0 and 1
+#' @param EQS_alpha the default value is 0.05, but could be specified as any value between 0 and 1
 #' @param moment_setting the default is NULL, if customize momentFUN please provide moment_setting=list(mu=, sigma=) 
 #' @param main title used in the plot.
 #' @param plot_type define the plot_type, default is "l"
@@ -53,13 +56,15 @@
 #' @author Xinran Zhao
 #' @export plotFrontiers
 #' 
-plotFrontiers <- function(R, frontiers, risk, ES_alpha = 0.05, CSM_alpha = 0.05, moment_setting = NULL, main="Efficient Frontiers", plot_type = "l", cex.axis=0.5, element.color="darkgray", legend.loc=NULL, legend.labels=NULL, cex.legend=0.8, xlim=NULL, ylim=NULL, ..., labels.assets=TRUE, pch.assets=21, cex.assets=0.8, col=NULL, lty=NULL, lwd=NULL){
+plotFrontiers <- function(R, frontiers, risk, ES_alpha = 0.05, CSM_alpha = 0.05, EQS_alpha = 0.05, moment_setting = NULL, main="Efficient Frontiers", plot_type = "l", cex.axis=0.5, element.color="darkgray", legend.loc=NULL, legend.labels=NULL, cex.legend=0.8, xlim=NULL, ylim=NULL, ..., labels.assets=TRUE, pch.assets=21, cex.assets=0.8, col=NULL, lty=NULL, lwd=NULL){
   if(risk %in% c('StdDev', 'var', 'stdDev')){
     risk = 'StdDev'
   } else if(risk %in% c('etl', 'ES', 'es')){
     risk = 'ES'
   } else if(risk %in% c('CSM', 'csm')){
     risk = 'CSM'
+  } else if(risk %in% c('EQS', 'eqs')){
+    risk = 'EQS'
   } else {print('please give the right risk type')}
 
   wname = paste0('w.', colnames(R))
@@ -74,7 +79,7 @@ plotFrontiers <- function(R, frontiers, risk, ES_alpha = 0.05, CSM_alpha = 0.05,
     mean_value = c()
     risk_value = c()
     for(j in 1:dim(w)[1]){
-      risk_measures = extract_risk(R, w[j,], ES_alpha = ES_alpha, CSM_alpha = CSM_alpha, moment_setting = moment_setting)
+      risk_measures = extract_risk(R, w[j,], ES_alpha = ES_alpha, CSM_alpha = CSM_alpha, EQS_alpha = EQS_alpha, moment_setting = moment_setting)
       mean_value = append(mean_value, risk_measures$mean)
       risk_value = append(risk_value, risk_measures[risk])
     }