[Returnanalytics-commits] r2064 - pkg/PerformanceAnalytics/sandbox/Meucci/R

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

Author: mkshah
Date: 2012-06-24 23:44:43 +0200 (Sun, 24 Jun 2012)
New Revision: 2064

Modified:
   pkg/PerformanceAnalytics/sandbox/Meucci/R/Prior2Posterior.R
Log:
Moving common functions to EntropyProg.R

Modified: pkg/PerformanceAnalytics/sandbox/Meucci/R/Prior2Posterior.R
===================================================================
--- pkg/PerformanceAnalytics/sandbox/Meucci/R/Prior2Posterior.R	2012-06-24 21:35:56 UTC (rev 2063)
+++ pkg/PerformanceAnalytics/sandbox/Meucci/R/Prior2Posterior.R	2012-06-24 21:44:43 UTC (rev 2064)
@@ -40,54 +40,4 @@
     # xlim( cbind( xl , xh ) )
     legend(x = 1.5, y =0.4 ,legend=c("analytical","prior"), lwd=c(0.2,0.2), lty=c(1,1), col=c("red", "blue"))
   }
-}
-
-
-#' Calculate the full-confidence posterior distributions of Mu and Sigma
-#'
-#' @param M     a numeric vector with the Mu of the normal reference model
-#' @param Q     a numeric vector used to construct a view on expectation of the linear combination Q %*% X
-#' @param M_Q   a numeric vector with the view of the expectations of QX
-#' @param S     a covariance matrix for the normal reference model
-#' @param G     a numeric vector used to construct a view on covariance of the linear combination G %*% X
-#' @param S_G   a numeric with the expectation associated with the covariance of the linear combination GX
-#'
-#' @return a list with 
-#'             M_   a numeric vector with the full-confidence posterior distribution of Mu
-#'             S_   a covariance matrix with the full-confidence posterior distribution of Sigma
-#'
-#' @references 
-#' \url{http://www.symmys.com}
-#' See Meucci script Prior2Posterior.m attached to Entropy Pooling Paper
-#' @author Ram Ahluwalia \email{ram@@wingedfootcapital.com}
-Prior2Posterior = function( M , Q , M_Q , S , G , S_G )
-{  
-  # See Appendix A.1 formula 49 for derivation
-  M_ = M + S %*% t(Q) %*% solve( Q %*% S %*% t(Q) ) %*% (M_Q - Q %*% M)
-    
-  # See Appendix A.1 formula 57 for derivation
-  S_= S + (S %*% t(G)) %*% ( solve( G %*% S %*% t(G) ) %*% S_G %*% solve( G %*% S %*% t(G) ) - solve( G%*%S%*%t(G) ) ) %*% ( G %*% S )
-    
-  return ( list( M_ = M_ , S_ = S_ ) )    
-}
-
-pHist = function( X, p, nBins )
-{
-  bins = seq(from = min(X), to = max(X), by = (max(X) - min(X))/nBins)
-  histObject1 <- hist( X[,1], breaks = bins, plot = FALSE )
-  histObject2 <- hist( X[,2], breaks = bins, plot = FALSE )
-  x = as.matrix(histObject1$mids)
-  n = cbind( histObject1$counts, histObject2$counts )
-  D = x[2,] - x[1,]
-  np = zeros( length(x), 1 )
-  for( s in 1:length(x) ) {
-    pVector = NULL
-    for( i in 1:nrow(X) ) {
-      if( ( X[i,1] >= (x[s,] - D/2) & X[i,1] <= (x[s,] + D/2) ) | ( X[i,2] >= (x[s,] - D/2) & X[i,2] <= (x[s,] + D/2) ) ) {
-        pVector = rbind(pVector, p[i,] )
-      }    
-    }
-    if( length(pVector) != 0 ) { np[s,] = sum( pVector ) }
-    f = np/D
-  }
 }
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