From 3cab7807e4d31a1cda940b9b3e160a1a7cf10a09 Mon Sep 17 00:00:00 2001
From: Guillaume Horel <guillaume.horel@serenitascapital.com>
Date: Thu, 30 Jul 2015 15:16:39 -0400
Subject: improve docs

---
 R/distrib.R | 52 ++++++++++++++++++++++++++--------------------------
 1 file changed, 26 insertions(+), 26 deletions(-)

(limited to 'R/distrib.R')

diff --git a/R/distrib.R b/R/distrib.R
index 536ed20..d941bd2 100644
--- a/R/distrib.R
+++ b/R/distrib.R
@@ -16,7 +16,7 @@
 #' Gaussian distribution.
 #'
 #' if f is a function, then with(GHquad(100), crossprod(f(Z), w))
-#'
+#' will compute \eqn{\frac{1}{\sqrt{2\pi}}\int_-\infty^\infty f(x)e^{-\frac{x^2}{2}}\,dx}.
 #' @param n Integer, the number of nodes
 #' @return A list with two components:
 #'  \item{Z}{the list of nodes}
@@ -106,20 +106,19 @@ lossdistrib2 <- function(p, w, S, N, defaultflag=FALSE){
     return(q)
 }
 
+#' recursive algorithm with first order correction truncated version
+#' this is actually slower than lossdistrib2. But in C this is
+#' twice as fast.
+#' For high severities, M can become bigger than N, and there is
+#' some probability mass escaping.
+#'
+#' @param p Numeric, vector of default probabilities
+#' @param w Numeric, vector of weights
+#' @param S Numeric, vector of severities
+#' @param N Integer, number of ticks in the grid
+#' @param cutoff Integer, where to stop computing the exact probabilities
+#' @return a Numeric vector of size \code{N} computing the loss distribution
 lossdistrib2.truncated <- function(p, w, S, N, cutoff=N){
-    ## recursive algorithm with first order correction
-    ## p vector of default probabilities
-    ## w vector of weigths
-    ## S vector of severities
-    ## N number of ticks in the grid (for best accuracy should
-    ## be a multiple of the number of issuers)
-    ## cutoff where to stop computing the exact probabilities
-    ## (useful for tranche computations)
-
-    ## this is actually slower than lossdistrib2. But in C this is
-    ## twice as fast.
-    ## for high severities, M can become bigger than N, and there is
-    ## some probability mass escaping.
     n <- length(p)
     lu <- 1/(N-1)
     q <- rep(0, cutoff)
@@ -174,19 +173,20 @@ recovdist <- function(dp, pp, w, S, N){
     return(q)
 }
 
+#' recursive algorithm with first order correction to compute the joint
+#' probability distribution of the loss and recovery.
+#' For high severities, M can become bigger than N, and there is
+#' some probability mass escaping.
+#'
+#' @param p Numeric, vector of default probabilities
+#' @param w Numeric, vector of weights
+#' @param S Numeric, vector of severities
+#' @param N Integer, number of ticks in the grid
+#' @param cutoff Integer, where to stop computing the exact probabilities
+#' @return q Matrix of joint loss, recovery probability distribution
+#' colSums(q) is the recovery distribution marginal
+#' rowSums(q) is the loss distribution marginal
 lossdist.joint <- function(p, w, S, N, defaultflag=FALSE){
-    ## recursive algorithm with first order correction
-    ## to compute the joint probability distribution of the loss and recovery
-    ## inputs:
-    ##   p: vector of default probabilities
-    ##   w: vector of issuer weights
-    ##   S: vector of severities
-    ##   N: number of tick sizes on the grid
-    ##   defaultflag: if true computes the default distribution
-    ## output:
-    ##   q: matrix of joint loss, recovery probability
-    ##      colSums(q) is the recovery distribution marginal
-    ##      rowSums(q) is the loss distribution marginal
     n <- length(p)
     lu <- 1/(N-1)
     q <- matrix(0, N, N)
-- 
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