optimize.portfolio"plot"(x, ..., return.col = "mean", risk.col = "ES", chart.assets = FALSE, neighbors = NULL, main = "optimized portfolio plot", xlim = NULL, ylim = NULL)
"plot"(x, ..., rp = FALSE, return.col = "mean", risk.col = "ES", chart.assets = FALSE, cex.axis = 0.8, element.color = "darkgray", neighbors = NULL, main = "GenSA.Portfolios", xlim = NULL, ylim = NULL)
"plot"(x, ..., return.col = "mean", risk.col = "ES", chart.assets = FALSE, cex.axis = 0.8, element.color = "darkgray", neighbors = NULL, main = "PSO.Portfolios", xlim = NULL, ylim = NULL)
"plot"(x, ..., rp = FALSE, risk.col = "ES", return.col = "mean", chart.assets = FALSE, element.color = "darkgray", neighbors = NULL, main = "ROI.Portfolios", xlim = NULL, ylim = NULL)
"plot"(x, ..., return.col = "mean", risk.col = "ES", chart.assets = FALSE, neighbors = NULL, xlim = NULL, ylim = NULL, main = "optimized portfolio plot")
"plot"(x, ..., return.col = "mean", risk.col = "ES", chart.assets = FALSE, neighbors = NULL, xlim = NULL, ylim = NULL, main = "optimized portfolio plot")optimize.portfoliorandom_portfoliostitlecex.return.col must be the name of a function used to compute the return metric on the random portfolio weights
risk.col must be the name of a function used to compute the risk metric on the random portfolio weightsneighbors may be specified in three ways.
The first is as a single number of neighbors. This will extract the neighbors closest
portfolios in terms of the out numerical statistic.
The second method consists of a numeric vector for neighbors.
This will extract the neighbors with portfolio index numbers that correspond to the vector contents.
The third method for specifying neighbors is to pass in a matrix.
This matrix should look like the output of extractStats, and should contain
risk.col,return.col, and weights columns all properly named.
The ROI and GenSA solvers do not store the portfolio weights like DEoptim or random
portfolios, random portfolios can be generated for the scatter plot with the
rp argument.