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...
166 search results for "PCA"
Rcpp reaches 100 dependents on CRAN
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 ...
Decluttering ordination plots in vegan part 2: orditorp()
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 →
Decluttering ordination plots in vegan part 1: ordilabel()
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 →
Factor Analysis of Baseball’s Hall of Fame Voters
Recently, Nate Silver wrote a post which analyzed how voters who voted for and against Barry Bonds for Baseball's Hall of Fame differed. Not surprisingly, those who voted for Bonds were more likely to vote for other suspected steroids users (like Roger Clemens). This got...
More Principal Components Fun
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 
100 most read R posts in 2012 (stats from R-bloggers) – big data, visualization, data manipulation, and other languages
Clustering with selected Principal Components
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 
Visualizing Principal Components
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, 
Visualising Tourism Data using R with googleVis package
Inspired by Mages’s post on Accessing and plotting World bank data with R (using googleVis package), I created one visualising tourism receipts and international tourist arrivals of various countries since 1995. The data used are from the World Bank’s country indicators. To see the motion chart, double click a picture below. Code Filed under: R, Tourism
Zero Inflated Models and Generalized Linear Mixed Models with R.
Zuur, Saveliev, Ieno (2012).
