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 reduces the dimensionality of multivariate data by creating few new key explanatory variables called principal components (PCs).
Each PC accounts for as much variance in the data as possible, provided that all the PAs are uncorrelated: therefore all PCs are independent and orthogonal.
It is possible to order the PCs according to the amount of total variation they explain, as well as to determine the relative contribution of each of the original variables to each PA.
A practical example follows using the software R on the “iris” dataset: Continue reading