![Extended (Simple) ASN Graph Visualization Example [R to D3]](https://rud.is/b/wp-content/uploads/2013/02/vc_60-300x231.png)
The small igraph visualization in the previous post shows the basics of what you can do with the BulkOrigin & BulkPeer functions, and I thought a larger example with some basic D3 tossed in might be even more useful. Assuming you have the previous functions in your environment, the following builds a larger graph structure
That is what I read the other day. For calculation of descriptive statistics, values below the LLOQ (lower limit of quantification) were set to.... Then I wondered, wasn't there a trick in JAGS to incorporate the presence of missing data while es...
With the arrival earlier today of the stochvol package onto the CRAN network for R, our Rcpp project reached a new milestone: 100 packages have either a Depends:, Imports: or LinkingTo: statement on it. The full list will always be at the bottom of ...
In the earlier post in this series I looked at the ordilabel() function to help tidy up ordination biplots in vegan. An alternative function vegan provides is orditorp(), the last four letters abbreviating the words text or points. That is … Continue reading →
In an earlier post I showed how to customise ordination diagrams produced by our vegan package for R through use of colours and plotting symbols. In a series of short posts I want to cover some of the options available … Continue reading →
Today, I want to continue with the Principal Components theme and show how the Principal Component Analysis can be used to build portfolios that are not correlated to the market. Most of the content for this post is based on the excellent article, “Using PCA for spread trading” by Jev Kuznetsov. Let’s start by loading 
In the Visualizing Principal Components post, I looked at the Principal Components of the companies in the Dow Jones Industrial Average index over 2012. Today, I want to show how we can use Principal Components to create Clusters (i.e. form groups of similar companies based on their distance from each other) Let’s start by loading 
Principal Component Analysis (PCA) is a procedure that converts observations into linearly uncorrelated variables called principal components (Wikipedia). The PCA is a useful descriptive tool to examine your data. Today I will show how to find and visualize Principal Components. Let’s look at the components of the Dow Jones Industrial Average index over 2012. First, 
Zero Inflated Models and Generalized Linear Mixed Models with R.
Zuur, Saveliev, Ieno (2012).
