[Returnanalytics-commits] r2192 - pkg/PerformanceAnalytics/sandbox/Meucci/R

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

Author: mkshah
Date: 2012-07-23 02:11:22 +0200 (Mon, 23 Jul 2012)
New Revision: 2192

Modified:
   pkg/PerformanceAnalytics/sandbox/Meucci/R/DetectOutliersviaMVE.R
   pkg/PerformanceAnalytics/sandbox/Meucci/R/RankingInformation.R
   pkg/PerformanceAnalytics/sandbox/Meucci/R/RobustBayesianAllocation.R
Log:
Updating comments and correcting code

Modified: pkg/PerformanceAnalytics/sandbox/Meucci/R/DetectOutliersviaMVE.R
===================================================================
--- pkg/PerformanceAnalytics/sandbox/Meucci/R/DetectOutliersviaMVE.R	2012-07-22 23:58:54 UTC (rev 2191)
+++ pkg/PerformanceAnalytics/sandbox/Meucci/R/DetectOutliersviaMVE.R	2012-07-23 00:11:22 UTC (rev 2192)
@@ -23,7 +23,7 @@
   library( matlab )
     
   # parameter checks
-  if ( ncol( corruptSample ) > nrow( corruptSample ) ) { stop("The number of assets must be greater than number of observations (otherwise system is singular)") }    
+  if ( ncol( sample ) > nrow( sample ) ) { stop("The number of assets must be greater than number of observations (otherwise system is singular)") }    
     
   # initialize parameters
   T = nrow( sample )
@@ -203,6 +203,7 @@
 #' @param    numGoodSamples      number of observations drawn from the covariance matrix
 #' @param    numOutliers         number of outliers added to sample
 #' @param    covarianceMatrix    the covariance matrix for the asset returns from which good samples will be drawn
+#' @param    shuffle             a boolean suggesting whether order of the twos should be shuffled
 #'
 #' @return   sample           a matrix of returns consisting of good and bad sample. Rows are observations, columns are the assets.
 #' @author Ram Ahluwalia \email{ram@@wingedfootcapital.com}

Modified: pkg/PerformanceAnalytics/sandbox/Meucci/R/RankingInformation.R
===================================================================
--- pkg/PerformanceAnalytics/sandbox/Meucci/R/RankingInformation.R	2012-07-22 23:58:54 UTC (rev 2191)
+++ pkg/PerformanceAnalytics/sandbox/Meucci/R/RankingInformation.R	2012-07-23 00:11:22 UTC (rev 2192)
@@ -6,8 +6,8 @@
 # TODO: confirm QuadProg does not have a bug (i.e. it can optimize expected returns without use dvec by adding an equality constraint)
 
 #' Generate a Stacked Bar Chart based on the frontier weights matrix
-#' @param      a matrix of weights where rows are efficient portfolios summing to one, and columns are assets
-#' @param      a string indicating the title of the chart
+#'
+#' @param weightsMatrix     a matrix of weights where rows are efficient portfolios summing to one, and columns are assets
 StackedBarChart = function( weightsMatrix )
 {
   data = as.data.frame( weightsMatrix )
@@ -18,6 +18,9 @@
 }
 
 #' view the rankings
+#'
+#' @param  X        a vector containing returns for all the asset classes
+#' @param  p        a vector containing the prior probability values
 #' @param  Lower    a vector of indexes indicating which column is lower than the corresponding column number in Upper
 #' @param  Upper    a vector of indexes indicating which column is lower than the corresponding column number in Upper
 #' @export EntropyProg

Modified: pkg/PerformanceAnalytics/sandbox/Meucci/R/RobustBayesianAllocation.R
===================================================================
--- pkg/PerformanceAnalytics/sandbox/Meucci/R/RobustBayesianAllocation.R	2012-07-22 23:58:54 UTC (rev 2191)
+++ pkg/PerformanceAnalytics/sandbox/Meucci/R/RobustBayesianAllocation.R	2012-07-23 00:11:22 UTC (rev 2192)
@@ -179,13 +179,13 @@
 #' \\ \mu_{1}  \equiv \frac{1}{ T_{1} }  \big( T_{0}   \mu_{0}  + T \hat{ \mu } \big)  
 #' \\ \nu_{1}  \equiv \nu_{0} + T
 #' \\ \Sigma_{1}  \equiv  \big(  \nu_{0} \Sigma_{0}  + T \hat{ \Sigma } +  \frac{ \big(\mu_{0}  - \hat{\mu} \big) \big(\mu_{0}  - \hat{\mu} \big)' }{ \big( \frac{1}{T} +  \frac{1}{T_{0} }  \big) } }
-#' @param mean                  the mean of the sample returns
-#' @param cov                   the sample covariance matrix
-#' @param mean_prior            the prior for the mean returns
-#' @param cov_prior             the covariance matrix prior
-#' @param confidenceInMeanPrior a numeric with the relative confidence in the mean prior vs. the sample mean. A value of 2 indicates twice as much weight to assign to the prior vs. the sample data. Must be greater than or equal to zero
-#' @param confidenceInCovPrior  a numeric with the relative confidence in the covariance prior vs. the sample covariance. A value of 2 indicates twice as much weight to assign to the prior vs. the sample data. Must be greater than or equal to zero
-#' @param sampleSize            a numeric with the number of rows in the sample data used to estimate mean_sample and cov_sample
+#' @param mean_sample                   the mean of the sample returns
+#' @param cov_sample                    the sample covariance matrix
+#' @param mean_prior                    the prior for the mean returns
+#' @param cov_prior                     the covariance matrix prior
+#' @param relativeConfidenceInMeanPrior a numeric with the relative confidence in the mean prior vs. the sample mean. A value of 2 indicates twice as much weight to assign to the prior vs. the sample data. Must be greater than or equal to zero
+#' @param relativeConfidenceInCovPrior  a numeric with the relative confidence in the covariance prior vs. the sample covariance. A value of 2 indicates twice as much weight to assign to the prior vs. the sample data. Must be greater than or equal to zero
+#' @param sampleSize                    a numeric with the number of rows in the sample data used to estimate mean_sample and cov_sample
 #'
 #' @return mean_post            a vector with the confidence weighted posterior mean vector of asset returns blended from the prior and sample mean vector
 #' @return cov_post             a covariance matrix the confidence weighted posterior covariance matrix of asset returns blended from the prior and sample covariance matrix