#R rarify samples code#

As an example, a dataset with 24 variables may be reduced to five principal components that represent the main patterns of variation amongst samples.

to create new axes that represent most of the variation in the data. When you use an ordination method, you are taking a set of variables and creating new principal axes along which samples (sites etc.) are scored or ordered (Gotelli and Ellison 2004), in order to reduce or simplify the data, i.e. As such, while ordination methods are very useful for helping simplify and make sense of multivariate data, careful consideration of why the methods are being used and which ones are most appropriate is necessary for strong ecological analyses! Interpretation of ordination analyses can be more difficult, especially if you are unsure of the specific questions you wish to explore with a particular ordination method.

#R rarify samples code#

R code for ordination techniques is readily available and relatively straight-forward to implement for most data. Ecologists are often told to вЂњrun a PCAвЂќ in the face of complex and messy multivariate data. Ordination is a set of methods for depicting samples in multiple dimensions (Clarke and Warwick 2011) and often feels like a catch-all term in ecological statistics.

choose ( ) ) # use coldiss.R which you have downloaded to your own directory What is ordination? packages ( 'ape' ) library (vegan ) library (gclus ) library (ape ) source ( file. | Load the required packages and functions install.