whitespace

1 files changed, 46 insertions, 46 deletions

diff --git a/R/distrib.R b/R/distrib.R
index ae5e51f..7dfafdd 100644
--- a/R/distrib.R
+++ b/R/distrib.R
@@ -49,9 +49,9 @@ lossdistrib <- function(p){
     n <- length(p)
     q <- rep(0, (n+1))
     q[1] <- 1
-    for(i in 1:n){
-        q[-1] <- p[i]*q[-(n+1)]+(1-p[i])*q[-1]
-        q[1] <- (1-p[i])*q[1]
+    for(i in 1:n) {
+        q[-1] <- p[i] * q[-(n+1)] + (1 - p[i]) * q[-1]
+        q[1] <- (1 - p[i]) * q[1]
     }
     return(q)
 }
@@ -86,9 +86,9 @@ convolve <- function(dist1, dist2) {
 #' @export
 lossdistrib.fft <- function(p) {
     ## haven't tested when p is not a power of 2.
-    if(length(p) == 1){
-        c(1-p, p)
-    }else {
+    if(length(p) == 1) {
+        c(1 - p, p)
+    } else {
         convolve(lossdistrib.fft(p[1:(length(p)/2)]),
                  lossdistrib.fft(p[-(1:(length(p)/2))]))
     }
@@ -121,7 +121,7 @@ lossdistrib2 <- function(p, w, S, N, defaultflag = FALSE){
     for(i in 1:n){
         if(defaultflag){
             d <- w[i] /lu
-        }else{
+        } else {
             d <- S[i] * w[i] / lu
         }
         d1 <- floor(d)
@@ -238,10 +238,10 @@ lossdist.joint <- function(p, w, S, N, defaultflag = FALSE){
     lu <- 1/(N-1)
     q <- matrix(0, N, N)
     q[1,1] <- 1
-    for(k in 1:n){
-        if(defaultflag){
+    for(k in 1:n) {
+        if(defaultflag) {
             x <- w[k] / lu
-        }else{
+        } else {
             x <- S[k] * w[k]/lu
         }
         y <- (1-S[k]) * w[k]/lu
@@ -299,7 +299,7 @@ lossdist.prepay.joint <- function(dp, pp, w, S, N, defaultflag = FALSE){
         if(defaultflag){
             weights2 <- c((i+1-x)*(j2+1-y2), (i+1-x)*(y2-j2), (x-i)*(y2-j2), (j2+1-y2)*(x-i))
             ppsplit <- pp[k] * weights2
-        }else{
+        } else {
             ppsplit <- pp[k] * c(j2+1-y2, y2-j2)
         }
         qtemp <- matrix(0, N, N)
@@ -312,7 +312,7 @@ lossdist.prepay.joint <- function(dp, pp, w, S, N, defaultflag = FALSE){
             qtemp[(i+1):N,(j2+2):N] <- qtemp[(i+1):N,(j2+2):N] + ppsplit[2] * q[1:(N-i), 1:(N-j2-1)]
             qtemp[(i+2):N,(j2+2):N] <- qtemp[(i+2):N,(j2+2):N] + ppsplit[3] * q[1:(N-i-1), 1:(N-j2-1)]
             qtemp[(i+2):N,(j2+1):N] <- qtemp[(i+2):N, (j2+1):N] + ppsplit[4] * q[1:(N-i-1), 1:(N-j2)]
-        }else{
+        } else {
             qtemp[, (j2+1):N] <- qtemp[,(j2+1):N]+ppsplit[1]*q[,1:(N-j2)]
             qtemp[, (j2+2):N] <- qtemp[,(j2+2):N]+ppsplit[2]*q[,1:(N-j2-1)]
         }
@@ -410,7 +410,7 @@ lossrecovdist <- function(defaultprob, prepayprob, w, S, N, defaultflag=FALSE, u
     if(missing(prepayprob)){
         L <- lossdistrib2(defaultprob, w, S, N, defaultflag)
         R <- lossdistrib2(defaultprob, w, 1-S, N)
-    }else{
+    } else {
         L <- lossdistrib2(defaultprob+defaultflag*prepayprob, w, S, N, defaultflag)
         R <- recovdist(defaultprob, prepayprob, w, S, N)
     }
@@ -422,14 +422,14 @@ lossrecovdist.term <- function(defaultprob, prepayprob, w, S, N, defaultflag=FAL
     ## computes the loss and recovery distribution over time
     L <- array(0, dim=c(N, ncol(defaultprob)))
     R <- array(0, dim=c(N, ncol(defaultprob)))
-    if(missing(prepayprob)){
+    if(missing(prepayprob)) {
         for(t in 1:ncol(defaultprob)){
             temp <- lossrecovdist(defaultprob[,t], , w, S[,t], N, defaultflag, useC)
             L[,t] <- temp$L
             R[,t] <- temp$R
         }
-    }else{
-        for(t in 1:ncol(defaultprob)){
+    } else {
+        for(t in 1:ncol(defaultprob)) {
             temp <- lossrecovdist(defaultprob[,t], prepayprob[,t], w, S[,t], N, defaultflag, useC)
             L[,t] <- temp$L
             R[,t] <- temp$R
@@ -462,11 +462,11 @@ dist.transform <- function(dist.joint) {
     ## from the (L, R) distribution using D = L+R
     distDR <- array(0, dim=dim(dist.joint))
     Ngrid <- dim(dist.joint)[2]
-    for(t in 1:dim(dist.joint)[1]){
+    for(t in 1:dim(dist.joint)[1]) {
         for(i in 1:Ngrid){
             for(j in 1:Ngrid){
                 index <- i+j
-                if(index <= Ngrid){
+                if(index <= Ngrid) {
                     distDR[t,index,j] <- distDR[t,index,j] + dist.joint[t,i,j]
                 }else{
                     distDR[t,Ngrid,j] <- distDR[t,Ngrid,j] +
@@ -485,13 +485,13 @@ shockprob <- function(p, rho, Z, log.p=F) {
   ## function is vectorized provided the below caveats:
   ## p and rho are vectors of same length n, Z is a scalar, returns vector of length n
   ## p and rho are scalars, Z is a vector of length n, returns vector of length n
-  if(length(p)==1){
-    if(rho==1){
-      return(Z<=qnorm(p))
-    }else{
+  if(length(p)==1) {
+    if(rho==1) {
+      return(Z <= qnorm(p))
+    } else {
       return(pnorm((qnorm(p)-sqrt(rho)*Z)/sqrt(1-rho), log.p=log.p))
     }
-  }else{
+  } else {
     result <- double(length(p))
     result[rho==1] <- Z<=qnorm(p[rho==1])
     result[rho<1] <- pnorm((qnorm(p[rho<1])-sqrt(rho[rho<1])*Z)/sqrt(1-rho[rho<1]), log.p=log.p)
@@ -504,8 +504,8 @@ shockseverity <- function(S, Stilde=1, Z, rho, p) {
   ## computes the severity as a function of the copula factor Z
   result <- double(length(S))
   result[p==0] <- 0
-  result[p!=0] <- Stilde * exp( shockprob(S[p!=0]/Stilde*p[p!=0], rho[p!=0], Z, TRUE) -
-           shockprob(p[p!=0], rho[p!=0], Z, TRUE))
+  result[p!=0] <- Stilde * exp( shockprob(S[p != 0] / Stilde * p[p != 0], rho[p != 0], Z, TRUE) -
+           shockprob(p[p != 0], rho[p != 0], Z, TRUE))
   return(result)
 }
 
@@ -515,7 +515,7 @@ dshockprob <- function(p,rho,Z) {
 }
 
 #' @export
-dqnorm <- function(x){
+dqnorm <- function(x) {
     1/dnorm(qnorm(x))
 }
 
@@ -523,23 +523,23 @@ dqnorm <- function(x){
 fit.prob <- function(Z, w, rho, p0) {
     ## if the weights are not perfectly gaussian, find the probability p such
     ## E_w(shockprob(p, rho, Z)) = p0
-    if(p0==0){
+    if(p0 == 0) {
         return(0)
     }
-    if(rho == 1){
+    if(rho == 1) {
         distw <- distr::DiscreteDistribution(Z, w)
         return(distr::pnorm(distr::q(distw)(p0)))
     }
     eps <- 1e-12
-    dp <- (crossprod(shockprob(p0,rho,Z),w)-p0)/crossprod(dshockprob(p0,rho,Z),w)
+    dp <- (crossprod(shockprob(p0, rho, Z),w) - p0) / crossprod(dshockprob(p0, rho, Z), w)
     p <- p0
-    while(abs(dp) > eps){
-        dp <- (crossprod(shockprob(p,rho,Z),w)-p0)/crossprod(dshockprob(p,rho,Z),w)
+    while(abs(dp) > eps) {
+        dp <- (crossprod(shockprob(p, rho, Z), w) - p0) / crossprod(dshockprob(p, rho, Z), w)
         phi <- 1
-        while ((p-phi*dp)<0 || (p-phi*dp)>1){
-            phi <- 0.8*phi
+        while ((p - phi * dp) < 0 || (p - phi * dp) > 1) {
+            phi <- 0.8 * phi
         }
-        p <- p - phi*dp
+        p <- p - phi * dp
     }
     return(p)
 }
@@ -556,16 +556,16 @@ fit.probC <- function(Z, w, rho, p0) {
 stochasticrecov <- function(R, Rtilde, Z, w, rho, porig, pmod) {
     ## if porig == 0 (probably matured asset) then return orginal recovery
     ## it shouldn't matter anyway since we never default that asset
-    if(porig == 0){
+    if(porig == 0) {
         return(rep(R, length(Z)))
-    }else{
+    } else {
         ptilde <- fit.prob(Z, w, rho, (1-R)/(1-Rtilde) * porig)
         return(abs(1-(1-Rtilde) * exp(shockprob(ptilde, rho, Z, TRUE) - shockprob(pmod, rho, Z, TRUE))))
     }
 }
 
 #' @export
-stochasticrecovC <- function(R, Rtilde, Z, w, rho, porig, pmod){
+stochasticrecovC <- function(R, Rtilde, Z, w, rho, porig, pmod) {
     r <- .C(C_stochasticrecov, as.double(R), as.double(Rtilde), as.double(Z),
             as.double(w), as.integer(length(Z)), as.double(rho), as.double(porig),
             as.double(pmod), q = double(length(Z)))
@@ -574,14 +574,14 @@ stochasticrecovC <- function(R, Rtilde, Z, w, rho, porig, pmod){
 
 #' @export
 BClossdist <- function(defaultprob, issuerweights, recov, rho, Z, w,
-                       N=length(recov)+1, defaultflag=FALSE, n.int=500){
+                       N = length(recov)+1, defaultflag = FALSE, n.int = 500) {
 
-    if(missing(Z)){
+    if(missing(Z)) {
         quadrature <- GHquad(n.int)
         Z <- quadrature$Z
         w <- quadrature$w
     }
-    stopifnot(length(Z)==length(w),
+    stopifnot(length(Z) == length(w),
               nrow(defaultprob) == length(issuerweights))
 
     ## do not use if weights are not gaussian, results would be incorrect
@@ -606,11 +606,11 @@ BClossdist <- function(defaultprob, issuerweights, recov, rho, Z, w,
 
 #' @export
 BClossdistC <- function(defaultprob, issuerweights, recov, rho, Z, w,
-                        N=length(issuerweights)+1, defaultflag=FALSE){
-    if(is.null(dim(defaultprob))){
+                        N = length(issuerweights)+1, defaultflag = FALSE) {
+    if(is.null(dim(defaultprob))) {
         dim(defaultprob) <- c(length(defaultprob),1)
     }
-    stopifnot(length(Z)==length(w),
+    stopifnot(length(Z) == length(w),
               nrow(defaultprob)==length(issuerweights),
               nrow(defaultprob)==length(recov))
     L <- matrix(0, N, ncol(defaultprob))
@@ -625,9 +625,9 @@ BClossdistC <- function(defaultprob, issuerweights, recov, rho, Z, w,
 
 #' @export
 BCER <- function(defaultprob, issuerweights, recov, K, rho, Z, w,
-                 N=length(issuerweights)+1, defaultflag=FALSE){
-    stopifnot(length(Z)==length(w),
-              nrow(defaultprob)==length(issuerweights))
+                 N = length(issuerweights)+1, defaultflag = FALSE) {
+    stopifnot(length(Z) == length(w),
+              nrow(defaultprob) == length(issuerweights))
 
     rho <- rep(rho, length(issuerweights))
     ELt <- numeric(ncol(defaultprob))