A More Aesthetic and Informative PCA Biplot
It is not an overstatement to say that the default output for a Principal Components Analysis (PCA) biplot using the biplot() function in R is gross. This presentation will show you how to use the output from the prcomp() function with the ggplot2 library to create a more aesthetically pleasing and informative PCA biplot.
Using Principal Components Analysis (PCA) to Analyze Latino Stress by Agricultural Season and Occupation
Principal Components Analysis (PCA) is a commonly used unsupervised machine learning technique. In this presentation, I describe the PCA method with a general description and geometric interpretation using simulated two and three-dimensional data. The description of the PCA methodology is followed by an application of PCA to analyze Latino stress by agricultural season and occupation in a majority-minority agricultural area of eastern Washington State.