Spurious Clusters in Granulometric Data Caused by Logratio Transformation

The logratio transformation aims to eliminate spurious correlations between components of compositional data. When applying this method to granulometric data, there arise numerical problems with zero (empty) components. In this paper, the method of logratio transformation with zero replacement is examined using one natural and two simulated granulometric data sets. The results show that this method generates spurious clusters, and thus it is not appropriate for the investigation of grain-size data in particular, and of compositional data with zero components in general.