NNS 11.1 Beta

Author: OVVO-Financial

NNS_11.1.tar.gz

@@ -5,7 +5,7 @@
 #'
 #' @param data A numeric matrix or data frame of variables to be clustered.
 #' @param degree Numeric options: (1, 2, 3). Degree of stochastic dominance test.
-#' @param type Character, either \code{"continuous"} (default) or \code{"discrete"}; specifies the type of CDF.
+#' @param type Character, either  \code{"discrete"} (default) or \code{"continuous"}; specifies the type of CDF.
 #' @param min_cluster Integer. The minimum number of elements required for a valid cluster.
 #' @param dendrogram Logical; \code{FALSE} (default). If \code{TRUE}, a dendrogram is produced based on a simple "distance" measure between clusters.
 #'
@@ -48,7 +48,7 @@
 #' @export
 
 
-NNS.SD.cluster <- function(data, degree = 1, type = "continuous", min_cluster = 1, dendrogram = FALSE) {
+NNS.SD.cluster <- function(data, degree = 1, type = "discrete", min_cluster = 1, dendrogram = FALSE) {
   clusters <- list()
   iteration <- 1
 

NNS_11.1.zip

@@ -48,19 +48,21 @@ NNS.ANOVA(control = x, treatment = y,
 ## ----stochdom, fig.width=7, fig.align='center'--------------------------------
 NNS.FSD(x, y)
 
-## ----stochdomset, eval=FALSE--------------------------------------------------
-#  set.seed(123)
-#  x1 = rnorm(1000)
-#  x2 = x1 + 1
-#  x3 = rnorm(1000)
-#  x4 = x3 + 1
-#  x5 = rnorm(1000)
-#  x6 = x5 + 1
-#  x7 = rnorm(1000)
-#  x8 = x7 + 1
-#  
-#  NNS.SD.efficient.set(cbind(x1, x2, x3, x4, x5, x6, x7, x8), degree = 1, status = FALSE)
-#  [1] "x4" "x2" "x8" "x6"
+## ----stochdomset, eval=TRUE---------------------------------------------------
+set.seed(123)
+x1 = rnorm(1000)
+x2 = x1 + 1
+x3 = rnorm(1000)
+x4 = x3 + 1
+x5 = rnorm(1000)
+x6 = x5 + 1
+x7 = rnorm(1000)
+x8 = x7 + 1
+
+NNS.SD.efficient.set(cbind(x1, x2, x3, x4, x5, x6, x7, x8), degree = 1, status = FALSE)
+
+## ----stochdomclust, eval=TRUE, fig.width=7, fig.align='center'----------------
+NNS.SD.cluster(cbind(x1, x2, x3, x4, x5, x6, x7, x8), degree = 1, dendrogram = TRUE)
 
 ## ----threads, echo = FALSE----------------------------------------------------
 Sys.setenv("OMP_THREAD_LIMIT" = "")

R/SD_Cluster.R

@@ -113,7 +113,7 @@ NNS.FSD(x, y)
 
 `x2, x4, x6, x8` all dominate their preceding distributions yet do not dominate one another, and are thus included in the first degree stochastic dominance efficient set.
 
-```{r stochdomset, eval=FALSE}
+```{r stochdomset, eval=TRUE}
 set.seed(123)
 x1 = rnorm(1000)
 x2 = x1 + 1
@@ -125,7 +125,15 @@ x7 = rnorm(1000)
 x8 = x7 + 1
 
 NNS.SD.efficient.set(cbind(x1, x2, x3, x4, x5, x6, x7, x8), degree = 1, status = FALSE)
-[1] "x4" "x2" "x8" "x6"
+```
+
+
+## Stochastic Dominant Clusters
+
+Further, we can assign clusters to non dominated constituents and represent the clustering in a dendrogram.
+
+```{r stochdomclust, eval=TRUE, fig.width=7, fig.align='center'}
+NNS.SD.cluster(cbind(x1, x2, x3, x4, x5, x6, x7, x8), degree = 1, dendrogram = TRUE)
 ```
 
 # References {#references}

doc/NNSvignette_Comparing_Distributions.R

@@ -616,8 +616,30 @@ <h2>Stochastic Dominant Efficient Sets</h2>
 <span id="cb18-8"><a href="#cb18-8" tabindex="-1"></a>x7 <span class="ot">=</span> <span class="fu">rnorm</span>(<span class="dv">1000</span>)</span>
 <span id="cb18-9"><a href="#cb18-9" tabindex="-1"></a>x8 <span class="ot">=</span> x7 <span class="sc">+</span> <span class="dv">1</span></span>
 <span id="cb18-10"><a href="#cb18-10" tabindex="-1"></a></span>
-<span id="cb18-11"><a href="#cb18-11" tabindex="-1"></a><span class="fu">NNS.SD.efficient.set</span>(<span class="fu">cbind</span>(x1, x2, x3, x4, x5, x6, x7, x8), <span class="at">degree =</span> <span class="dv">1</span>, <span class="at">status =</span> <span class="cn">FALSE</span>)</span>
-<span id="cb18-12"><a href="#cb18-12" tabindex="-1"></a>[<span class="dv">1</span>] <span class="st">&quot;x4&quot;</span> <span class="st">&quot;x2&quot;</span> <span class="st">&quot;x8&quot;</span> <span class="st">&quot;x6&quot;</span></span></code></pre></div>
+<span id="cb18-11"><a href="#cb18-11" tabindex="-1"></a><span class="fu">NNS.SD.efficient.set</span>(<span class="fu">cbind</span>(x1, x2, x3, x4, x5, x6, x7, x8), <span class="at">degree =</span> <span class="dv">1</span>, <span class="at">status =</span> <span class="cn">FALSE</span>)</span></code></pre></div>
+<pre><code>## [1] &quot;x4&quot; &quot;x2&quot; &quot;x8&quot; &quot;x6&quot;</code></pre>
+</div>
+<div id="stochastic-dominant-clusters" class="section level2">
+<h2>Stochastic Dominant Clusters</h2>
+<p>Further, we can assign clusters to non dominated constituents and
+represent the clustering in a dendrogram.</p>
+<div class="sourceCode" id="cb20"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb20-1"><a href="#cb20-1" tabindex="-1"></a><span class="fu">NNS.SD.cluster</span>(<span class="fu">cbind</span>(x1, x2, x3, x4, x5, x6, x7, x8), <span class="at">degree =</span> <span class="dv">1</span>, <span class="at">dendrogram =</span> <span class="cn">TRUE</span>)</span></code></pre></div>
+<p><img src="data:image/png;base64,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" style="display: block; margin: auto;" /></p>
+<pre><code>## $Clusters
+## $Clusters$Cluster_1
+## [1] &quot;x4&quot; &quot;x2&quot; &quot;x8&quot; &quot;x6&quot;
+## 
+## $Clusters$Cluster_2
+## [1] &quot;x3&quot; &quot;x1&quot; &quot;x7&quot; &quot;x5&quot;
+## 
+## 
+## $Dendrogram
+## 
+## Call:
+## hclust(d = dist_matrix, method = &quot;complete&quot;)
+## 
+## Cluster method   : complete 
+## Number of objects: 8</code></pre>
 </div>
 </div>
 <div id="references" class="section level1">

doc/NNSvignette_Comparing_Distributions.Rmd

@@ -119,7 +119,7 @@ NNS also offers a wrapper function **`NNS.ARMA.optim()`** to test a given vector
 Given our monthly dataset, we will try multiple years by setting `seasonal.factor = seq(12, 60, 6)` every 6 months based on our **NNS.seas()** insights above.
 
 ```{r best optim, eval=FALSE}
-nns.optimal = NNS.ARMA.optim(AirPassengers, 
+nns.optimal = NNS.ARMA.optim(AirPassengers,
                              training.set = 100, 
                              seasonal.factor = seq(12, 60, 6),
                              obj.fn = expression( sqrt(mean((predicted - actual)^2)) ),

doc/NNSvignette_Comparing_Distributions.html

@@ -501,7 +501,7 @@ <h2>Cross-Validating All Combinations of
 <p>Given our monthly dataset, we will try multiple years by setting
 <code>seasonal.factor = seq(12, 60, 6)</code> every 6 months based on
 our <strong>NNS.seas()</strong> insights above.</p>
-<div class="sourceCode" id="cb15"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb15-1"><a href="#cb15-1" tabindex="-1"></a>nns.optimal <span class="ot">=</span> <span class="fu">NNS.ARMA.optim</span>(AirPassengers, </span>
+<div class="sourceCode" id="cb15"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb15-1"><a href="#cb15-1" tabindex="-1"></a>nns.optimal <span class="ot">=</span> <span class="fu">NNS.ARMA.optim</span>(AirPassengers,</span>
 <span id="cb15-2"><a href="#cb15-2" tabindex="-1"></a>                             <span class="at">training.set =</span> <span class="dv">100</span>, </span>
 <span id="cb15-3"><a href="#cb15-3" tabindex="-1"></a>                             <span class="at">seasonal.factor =</span> <span class="fu">seq</span>(<span class="dv">12</span>, <span class="dv">60</span>, <span class="dv">6</span>),</span>
 <span id="cb15-4"><a href="#cb15-4" tabindex="-1"></a>                             <span class="at">obj.fn =</span> <span class="fu">expression</span>( <span class="fu">sqrt</span>(<span class="fu">mean</span>((predicted <span class="sc">-</span> actual)<span class="sc">^</span><span class="dv">2</span>)) ),</span>

doc/NNSvignette_Forecasting.Rmd

@@ -48,19 +48,21 @@ NNS.ANOVA(control = x, treatment = y,
 ## ----stochdom, fig.width=7, fig.align='center'--------------------------------
 NNS.FSD(x, y)
 
-## ----stochdomset, eval=FALSE--------------------------------------------------
-#  set.seed(123)
-#  x1 = rnorm(1000)
-#  x2 = x1 + 1
-#  x3 = rnorm(1000)
-#  x4 = x3 + 1
-#  x5 = rnorm(1000)
-#  x6 = x5 + 1
-#  x7 = rnorm(1000)
-#  x8 = x7 + 1
-#  
-#  NNS.SD.efficient.set(cbind(x1, x2, x3, x4, x5, x6, x7, x8), degree = 1, status = FALSE)
-#  [1] "x4" "x2" "x8" "x6"
+## ----stochdomset, eval=TRUE---------------------------------------------------
+set.seed(123)
+x1 = rnorm(1000)
+x2 = x1 + 1
+x3 = rnorm(1000)
+x4 = x3 + 1
+x5 = rnorm(1000)
+x6 = x5 + 1
+x7 = rnorm(1000)
+x8 = x7 + 1
+
+NNS.SD.efficient.set(cbind(x1, x2, x3, x4, x5, x6, x7, x8), degree = 1, status = FALSE)
+
+## ----stochdomclust, eval=TRUE, fig.width=7, fig.align='center'----------------
+NNS.SD.cluster(cbind(x1, x2, x3, x4, x5, x6, x7, x8), degree = 1, dendrogram = TRUE)
 
 ## ----threads, echo = FALSE----------------------------------------------------
 Sys.setenv("OMP_THREAD_LIMIT" = "")

doc/NNSvignette_Forecasting.html

@@ -113,7 +113,7 @@ NNS.FSD(x, y)
 
 `x2, x4, x6, x8` all dominate their preceding distributions yet do not dominate one another, and are thus included in the first degree stochastic dominance efficient set.
 
-```{r stochdomset, eval=FALSE}
+```{r stochdomset, eval=TRUE}
 set.seed(123)
 x1 = rnorm(1000)
 x2 = x1 + 1
@@ -125,7 +125,15 @@ x7 = rnorm(1000)
 x8 = x7 + 1
 
 NNS.SD.efficient.set(cbind(x1, x2, x3, x4, x5, x6, x7, x8), degree = 1, status = FALSE)
-[1] "x4" "x2" "x8" "x6"
+```
+
+
+## Stochastic Dominant Clusters
+
+Further, we can assign clusters to non dominated constituents and represent the clustering in a dendrogram.
+
+```{r stochdomclust, eval=TRUE, fig.width=7, fig.align='center'}
+NNS.SD.cluster(cbind(x1, x2, x3, x4, x5, x6, x7, x8), degree = 1, dendrogram = TRUE)
 ```
 
 # References {#references}