I stepped into a blog post by Pia Parolin titled “Do all biological processes need to be statistically significant?” ( http://tinyurl.com/nwcq5xa ). It sounds, at moments, the frustrated cry of the field biologist observing cool patterns and building cool theories on it until he/she faces that bloody p-value=0.051. Who hasn’t been there? Yet Pia’s article contains more than that, and it raises interesting issues (also see the article’s comments). Here are some notes of mine. Continue reading
Principal Component Analysis (PCA) is an ordination method that, given N explanatory variables, creates a new set of N explanatory variables with two main characteristics: 1) they are all orthogonal, therefore independent, to each other and 2) they are ranked by importance: the first PC is the one that explain the most variability, the Nth is the one that explains the least. Because of these features, PCA is sometimes used to reduce the dimensionality of multivariate data by selecting few the two or three PC that explains the most variability.
It is also possible to determine the relative contribution of each of the original variables to each PC.
A practical example follows using the software R on the “iris” dataset: Continue reading