GRASS: GRadient AnalySiS
In snwikaij/GRASS: GRadient AnalySiS for ecologists

Presence/absence data for taxa is often more prevalent than abundance. It is also more consistent and less variable than abundance data. For example, the abundance of fish schools or plant cover are very variable and notation of this factor is very dependent on sample effort and method. I started this R-package more for out of personal interest on how programming and data-analysis can quantify ecological phenomenon in stead than merely describe these. I also wanted to use methods that everyone could interpret easy even with minimal statistical background, the results: "GRASS".

The GRASS package has multiple functions. The basic of some functions relies on the Area Under the Curve (AUC). The AUC gives the probability random sample of taxon from distribution x will rank higher than a sample from distribution eqny. The AUC is adapted so that it can be used to analyse the variation of multiple taxa in an assembly around a single gradient (i.e. after multivariate analysis). This can be used to answer the question, "how likely are we to find a specific taxon deviating from the other taxa (residual assembly)." Functions using the AUC are a standard function AUC.test comparing two populations and AUCG.test, which compares taxa/groups in relation to the total assembly of all taxa/groups in the dataset. The AUCG.test also has the potential to perform a pairwise comparison. An overview can be plotted in the form of a boxplot with the boxpl function or create your own with R base or ggplot2.

The threshold indication functions are used to identify thresholds in presence/absence data. The functions use the cumulative sum principle to detect changes in presence/absence in relation to the sample distribution. These functions incorporate the AUC as selection criteria and can be changed upon the preferences of the user. The function cusTH performs the threshold detection and returns a data frame. This data frame can be plotted with the plotTH function. The process of detecting the thresholds can be visualized with the plotCUS function on the original dataset. However, a not of caution is necessary, although the word threshold implies a sudden change this threshold indication indicates a change in relation to the distribution of all samples in the total dataset.

P.S. Comments and ways to improve documentation and function performance are a welcome.

snwikaij/GRASS documentation built on July 29, 2020, 1:54 p.m.