[Returnanalytics-commits] r3779 - in pkg/Dowd: R man

[noreply at r-forge.r-project.org]

Author: dacharya
Date: 2015-07-05 22:33:01 +0200 (Sun, 05 Jul 2015)
New Revision: 3779

Added:
   pkg/Dowd/R/PCAPrelim.R
   pkg/Dowd/man/PCAPrelim.Rd
Log:
PCAPrelim added.

Added: pkg/Dowd/R/PCAPrelim.R
===================================================================
--- pkg/Dowd/R/PCAPrelim.R	                        (rev 0)
+++ pkg/Dowd/R/PCAPrelim.R	2015-07-05 20:33:01 UTC (rev 3779)
@@ -0,0 +1,68 @@
+#' PCAPrelim
+#' 
+#' Estimates VaR plot using principal components analysis
+#' 
+#' @param Ra Matrix return data set where each row is interpreted as a set of 
+#' daily observations, and each column as the returns to each position in a 
+#' portfolio
+#' @param position.data Position-size vector, giving amount invested in each 
+#' position
+#' @references Dowd, K. Measuring Market Risk, Wiley, 2007.
+#'
+#' @author Dinesh Acharya
+#' @examples
+#' 
+#'    # Computes PCA Prelim
+#'    Ra <- matrix(rnorm(4*4), 4, 4)
+#'    PCAPrelim(Ra)
+#'
+#' @import expm MASS
+#'
+#' @export
+PCAPrelim <- function(Ra){
+  rho <- as.matrix(Ra)
+  corr.matrix <- rbind(cbind(rho%^%0, rho%^%1, rho%^%2, rho%^%3, rho%^%4, 
+                             rho%^%5, rho%^%6, rho%^%7, rho%^%8, rho%^%9),
+                       cbind(rho%^%1, rho%^%0, rho%^%1, rho%^%2, rho%^%3,
+                             rho%^%4, rho%^%5, rho%^%6, rho%^%7, rho%^%8),
+                       cbind(rho%^%2, rho%^%1, rho%^%0, rho%^%1, rho%^%2,
+                             rho%^%3, rho%^%4, rho%^%5, rho%^%6, rho%^%7),
+                       cbind(rho%^%3, rho%^%2, rho%^%1, rho%^%0, rho%^%1,
+                             rho%^%2, rho%^%3, rho%^%4, rho%^%5, rho%^%6),
+                       cbind(rho%^%4, rho%^%3, rho%^%2, rho%^%1, rho%^%0,
+                             rho%^%1, rho%^%2, rho%^%3, rho%^%4, rho%^%5),
+                       cbind(rho%^%5, rho%^%4, rho%^%3, rho%^%2, rho%^%1,
+                             rho%^%0, rho%^%1, rho%^%2, rho%^%3, rho%^%4),
+                       cbind(rho%^%6, rho%^%5, rho%^%4, rho%^%3, rho%^%2,
+                             rho%^%1, rho%^%0, rho%^%1, rho%^%2, rho%^%3),
+                       cbind(rho%^%7, rho%^%6, rho%^%5, rho%^%4, rho%^%3,
+                             rho%^%2, rho%^%1, rho%^%0, rho%^%1, rho%^%2),
+                       cbind(rho%^%8, rho%^%7, rho%^%6, rho%^%5, rho%^%4,
+                             rho%^%3, rho%^%2, rho%^%1, rho%^%0, rho%^%1),
+                       cbind(rho%^%9, rho%^%8, rho%^%7, rho%^%6, rho%^%5,
+                             rho%^%4, rho%^%3, rho%^%2, rho%^%1, rho%^%0))
+  mu <- double(10)
+  sigma <- corr.matrix
+  # Random number generation
+  returns <- mvrnorm(1000, mu, sigma)
+  # Dowd code uses princomp in matlab. Similar function "princomp" is available 
+  # in "stats" package. However, the return values from princomp are not used
+  # explicitly. So, following alternative was used.
+  variances <- eigen(cov(returns))$values # eigenvalues of covariance matrix.
+  
+  # Scree Plot
+  n <- 1000
+  par(c(2,1))
+  percent.explained <- 100 * variances / sum(variances)
+  barplot(percent.explained, xlab = "%")
+  
+  cum.variance <- double(length(variances))
+  cum.variance[1] <- percent.explained[1]
+  for (i in 2:length(variances)) {
+    cum.variances[i] <- percent.explained[i] + cum.variance[i-1]
+  }
+  t <- 0:10
+  plot(t, c(0, cum_variance), xlab = "Principal component", ylab = "%")
+  title("Explanatory Power of the Principal Components")
+  
+}

Added: pkg/Dowd/man/PCAPrelim.Rd
===================================================================
--- pkg/Dowd/man/PCAPrelim.Rd	                        (rev 0)
+++ pkg/Dowd/man/PCAPrelim.Rd	2015-07-05 20:33:01 UTC (rev 3779)
@@ -0,0 +1,31 @@
+% Generated by roxygen2 (4.1.1): do not edit by hand
+% Please edit documentation in R/PCAPrelim.R
+\name{PCAPrelim}
+\alias{PCAPrelim}
+\title{PCAPrelim}
+\usage{
+PCAPrelim(Ra)
+}
+\arguments{
+\item{Ra}{Matrix return data set where each row is interpreted as a set of
+daily observations, and each column as the returns to each position in a
+portfolio}
+
+\item{position.data}{Position-size vector, giving amount invested in each
+position}
+}
+\description{
+Estimates VaR plot using principal components analysis
+}
+\examples{
+# Computes PCA Prelim
+   Ra <- matrix(rnorm(4*4), 4, 4)
+   PCAPrelim(Ra)
+}
+\author{
+Dinesh Acharya
+}
+\references{
+Dowd, K. Measuring Market Risk, Wiley, 2007.
+}
+