Other R packages and functions
Beals smoothing is a multivariate transformation specially designed for species presence/absence community data containing noise and/or a lot of zeros. This transformation replaces the observed values of the target species by predictions of occurrence on the basis of its co-occurrences with the remaining species. In many applications, the transformed values are used as input for multivariate analyses. As Beals smoothing values provide a sense of probability of occurrence, they have also been used for inference. However, this transformation can produce spurious results, and it must be used with caution. We include here a set of functions to conduct the transformation and to test its validity (De Cáceres et al. 2008).
Miquel De Cáceres, Jari Oksanen
Download and uncompress the following ZIP file. Includes an R script file and a user manual in PDF. See also function
beals in R package vegan.
R package STI
In order to test hypotheses about changes in the environment induced by man ecologists are sampling portions of the environment repeatedly across time. The STI R package implements a method for testing a space–time interaction in repeated ecological survey data, when there is no replication at the level of individual sampling units (sites) (Legendre et al. 2010). This methodological development is important for the analysis of long-term monitoring data. In these systems, an interaction may indicate that the spatial structure of community composition has changed in the course of time or that the temporal evolution is not the same at all sites. The solution to the problem that we suggest is based on the representation of space and time by principal coordinates of neighbor matrices (PCNMs) in the ANOVA. Function
STImodels performs two-way ANOVA to test space-time interaction without replicates using one among a set of possible models. Function
quickSTI allows performing space-time ANOVA in a simplified way. In many models, degrees of freedom are saved by coding space and/or time parsimoniously using PCNMs.
Pierre Legendre, Miquel De Cáceres, Daniel Borcard
Download and uncompress the following ZIP file to have both sources and compilations for windows and mac.