NNS 10.9.4 Beta

Author: OVVO-Financial

NNS_10.9.4.tar.gz

@@ -10,7 +10,7 @@
 #' @param exp numeric; \code{1} default will exponentially weight maximum rho value if \code{exp > 1}.  Shrinks values towards \code{upper_rho}.
 #' @param type options("spearman", "pearson", "NNScor", "NNSdep"); \code{type = "spearman"}(default) dependence metric desired.
 #' @param drift logical; \code{drift = TRUE} (default) preserves the drift of the original series.
-#' @param target_drift numerical; code{NULL} (default) Specifies the desired drift when \code{drift = TRUE}, i.e. a risk-free rate of return.
+#' @param target_drift numerical; code{target_drift = NULL} (default) Specifies the desired drift when \code{drift = TRUE}, i.e. a risk-free rate of return.
 #' @param xmin numeric; the lower limit for the left tail.
 #' @param xmax numeric; the upper limit for the right tail.
 #' @param ... possible additional arguments to be passed to \link{NNS.meboot}.
@@ -52,13 +52,18 @@ NNS.MC <- function(x,
 
   exp_rhos <- rev(c((neg_rhos^exp)*-1, pos_rhos^(1/exp)))
 
+  if(is.null(target_drift)){
+    n <- length(x)
+    orig_coef <- fast_lm(1:n, x)$coef
+    orig_intercept <- orig_coef[1]
+    orig_drift <- orig_coef[2]
+    target_drift <- orig_drift
+  }
 
-  samples <- suppressWarnings(NNS.meboot(x = x, reps = reps, rho = exp_rhos, type = type, 
-                                         drift = drift, target_drift = target_drift, xmin = xmin, xmax = xmax, ...))
 
-  replicates <- samples["replicates",]
-  
-  rm(samples)
+  replicates <- suppressWarnings(NNS.meboot(x = x, reps = reps, rho = exp_rhos, type = type, drift = drift,
+                                            target_drift = target_drift, xmin = xmin, xmax = xmax, ...))["replicates",]
+
 
   ensemble <- Rfast::rowmeans(do.call(cbind, replicates))
 

NNS_10.9.4.zip

@@ -7,7 +7,7 @@
 #' @param rho numeric [-1,1] (vectorized); A \code{rho} must be provided, otherwise a blank list will be returned.
 #' @param type options("spearman", "pearson", "NNScor", "NNSdep"); \code{type = "spearman"}(default) dependence metric desired.
 #' @param drift logical; \code{drift = TRUE} (default) preserves the drift of the original series.
-#' @param target_drift numerical; code{NULL} (default) Specifies the desired drift when \code{drift = TRUE}, i.e. a risk-free rate of return.
+#' @param target_drift numerical; code{target_drift = NULL} (default) Specifies the desired drift when \code{drift = TRUE}, i.e. a risk-free rate of return.
 #' @param trim numeric [0,1]; The mean trimming proportion, defaults to \code{trim = 0.1}.
 #' @param xmin numeric; the lower limit for the left tail.
 #' @param xmax numeric; the upper limit for the right tail.
@@ -41,6 +41,8 @@
 #' \item{kappa} scale adjustment to the variance of ME density.
 #' \item{elaps} elapsed time.
 #' }
+#' 
+#' @note Vectorized \code{rho} and \code{drift} parameters will not vectorize both simultaneously.  Also, do not specify \code{target_drift = NULL}.
 #'
 #' @references
 #' \itemize{
@@ -57,7 +59,7 @@
 #' @examples
 #' \dontrun{
 #' # To generate an orthogonal rank correlated time-series to AirPassengers
-#' boots <- NNS.meboot(AirPassengers, reps=100, rho = 0, xmin = 0)
+#' boots <- NNS.meboot(AirPassengers, reps = 100, rho = 0, xmin = 0)
 #'
 #' # Verify correlation of replicates ensemble to original
 #' cor(boots["ensemble",]$ensemble, AirPassengers, method = "spearman")
@@ -67,6 +69,17 @@
 #'
 #' # Plot ensemble
 #' lines(boots["ensemble",]$ensemble, lwd = 3)
+#' 
+#' 
+#' ### Vectorized drift with a single rho
+#' boots <- NNS.meboot(AirPassengers, reps = 100, rho = 0, xmin = 0, target_drift = c(1,7))
+#' matplot(do.call(cbind, boots["replicates", ]), type = "l")
+#' lines(1:length(AirPassengers), AirPassengers, lwd = 3, col = "red")
+#' 
+#' ### Vectorized rho with a single target drift
+#' boots <- NNS.meboot(AirPassengers, reps = 100, rho = c(0, .5, 1), xmin = 0, target_drift = 3)
+#' matplot(do.call(cbind, boots["replicates", ]), type = "l")
+#' lines(1:length(AirPassengers), AirPassengers, lwd = 3, col = "red") 
 #' }
 #' @export
 
@@ -107,7 +120,14 @@
     ordxx <- order(x)
 
 
-    ### Fred Viole SUGGESTION PART 1 of 2
+    if(is.null(target_drift)){
+      orig_coef <- fast_lm(1:n, x)$coef
+      orig_intercept <- orig_coef[1]
+      orig_drift <- orig_coef[2]
+      target_drift <- orig_drift
+    }
+    
+
 
     if(rho < 1){
       if(rho < -0.5) ordxx_2 <- rev(ordxx) else ordxx_2 <- order(ordxx)
@@ -194,11 +214,8 @@
 
     ensemble[ordxx,] <- qseq
 
-  
-    ### Fred Viole SUGGESTION  PART 2 of 2
-    ### Average two ordxx ensemble matrices
 
-    if(rho<1){
+    if(any(rho<1)){
       matrix2 = matrix(0, nrow=length(x), ncol = reps)
       matrix2[ordxx_2,] = qseq
 
@@ -240,9 +257,9 @@
 
       new_coef <- apply(ensemble, 2, function(i) fast_lm(1:n, i)$coef)
       slopes <- new_coef[2,]
-      
+     
       if(drift){
-        if(is.null(target_drift)) new_slopes <- (orig_drift - slopes) else new_slopes <- (target_drift - slopes)
+        new_slopes <- (target_drift - slopes)
         ensemble <- ensemble + t(t(sapply(new_slopes, function(slope) cumsum(rep(slope, n)))))
 
         new_intercepts <- orig_intercept - new_coef[1,]
@@ -303,4 +320,4 @@
     return(final)
  }
 
-NNS.meboot <- Vectorize(NNS.meboot, vectorize.args = "rho")
+NNS.meboot <- Vectorize(NNS.meboot, vectorize.args = c("rho", "target_drift"))

R/NNS_MC.R

@@ -151,6 +151,13 @@ legend('left', legend = c('ecdf', 'LPM.ratio'), fill=c('black','red'), border=NA
 #     rho = 1  rho = 0.5 rho = -0.5   rho = -1
 #   1.0000000  0.4989619 -0.4984818 -0.9779778
 
+## ----tgt_drift, fig.align='center', fig.width=8, fig.height=8, eval=FALSE-----
+#  boots = NNS.MC(x, reps = 1, lower_rho = -1, upper_rho = 1, by = .5, target_drift = 0.05)$replicates
+#  reps = do.call(cbind, boots)
+#  
+#  plot(x, type = "l", lwd = 3, ylim = c(min(reps), max(reps)))
+#  matplot(reps, type = "l", col = rainbow(length(boots)), add = TRUE)
+
 ## ----multisim, eval=FALSE-----------------------------------------------------
 #  set.seed(123)
 #  x <- rnorm(1000); y <- rnorm(1000); z <- rnorm(1000)

R/NNS_meboot.R

@@ -227,7 +227,22 @@ sapply(boots, function(r) cor(r, x, method = "spearman"))
  1.0000000  0.4989619 -0.4984818 -0.9779778
 ```
 
-More replicates and ensembles thereof can be generated for any number of $\rho$ values. Please see the full **`NNS.meboot`** and **`NNS.MC`** argument documentation.
+More replicates and ensembles thereof can be generated for any number of $\rho$ values. 
+
+### `target_drift` Specification
+We can also specify a target drift in our replicates with the `target_drift` parameter.
+
+```{r tgt_drift, fig.align='center', fig.width=8, fig.height=8, eval=FALSE}
+boots = NNS.MC(x, reps = 1, lower_rho = -1, upper_rho = 1, by = .5, target_drift = 0.05)$replicates
+reps = do.call(cbind, boots)
+
+plot(x, type = "l", lwd = 3, ylim = c(min(reps), max(reps)))
+matplot(reps, type = "l", col = rainbow(length(boots)), add = TRUE)
+```
+
+![](images/NNSmc_1_tgt_drift.png)
+
+Please see the full **`NNS.meboot`** and **`NNS.MC`** argument documentation.
 
 ## Simulating a Multivariate Dependence Structure
 

doc/NNSvignette_Sampling.R

@@ -151,6 +151,13 @@ legend('left', legend = c('ecdf', 'LPM.ratio'), fill=c('black','red'), border=NA
 #     rho = 1  rho = 0.5 rho = -0.5   rho = -1
 #   1.0000000  0.4989619 -0.4984818 -0.9779778
 
+## ----tgt_drift, fig.align='center', fig.width=8, fig.height=8, eval=FALSE-----
+#  boots = NNS.MC(x, reps = 1, lower_rho = -1, upper_rho = 1, by = .5, target_drift = 0.05)$replicates
+#  reps = do.call(cbind, boots)
+#  
+#  plot(x, type = "l", lwd = 3, ylim = c(min(reps), max(reps)))
+#  matplot(reps, type = "l", col = rainbow(length(boots)), add = TRUE)
+
 ## ----multisim, eval=FALSE-----------------------------------------------------
 #  set.seed(123)
 #  x <- rnorm(1000); y <- rnorm(1000); z <- rnorm(1000)

doc/NNSvignette_Sampling.Rmd

@@ -227,7 +227,22 @@ sapply(boots, function(r) cor(r, x, method = "spearman"))
  1.0000000  0.4989619 -0.4984818 -0.9779778
 ```
 
-More replicates and ensembles thereof can be generated for any number of $\rho$ values. Please see the full **`NNS.meboot`** and **`NNS.MC`** argument documentation.
+More replicates and ensembles thereof can be generated for any number of $\rho$ values. 
+
+### `target_drift` Specification
+We can also specify a target drift in our replicates with the `target_drift` parameter.
+
+```{r tgt_drift, fig.align='center', fig.width=8, fig.height=8, eval=FALSE}
+boots = NNS.MC(x, reps = 1, lower_rho = -1, upper_rho = 1, by = .5, target_drift = 0.05)$replicates
+reps = do.call(cbind, boots)
+
+plot(x, type = "l", lwd = 3, ylim = c(min(reps), max(reps)))
+matplot(reps, type = "l", col = rainbow(length(boots)), add = TRUE)
+```
+
+![](images/NNSmc_1_tgt_drift.png)
+
+Please see the full **`NNS.meboot`** and **`NNS.MC`** argument documentation.
 
 ## Simulating a Multivariate Dependence Structure