optimus

What is optimus??

An R package for assessment and diagnostics of competing clustering solutions, using predictive models. The main intended use is for comparing clustering/classification solutions of ecological data (e.g. presence/absence, counts, ordinal scores) to 1) find an optimal partitioning solution, 2) identify characteristic species and 3) refine a classification by merging clusters that increase predictive performance. However, in a more general sense, this package can do the above for any set of clustering solutions for i observations of j variables. More details on the background and theory behind using predictive models for classification assessment, in an ecological context, can be found in Lyons et al. (2016).

Development

This is a development version, and this is also the first package I have written for public access. There are certainly bug and issues, and if you find them, please let me know about them - either directly on github, or the contact details below.

Installation

I have not yet figured out how to host binary packages for optimus. If you have the correct dev tools and compilers on your system, then you can compile the package yourself from source (on github).

Honestly, the easiest way to install (until I get optimus on CRAN), is to use Hadley Wickham's (excellent) devtools package. Then it's easy as to install optimus within the R environment, directly from github. Simply install devtools from CRAN with

install.packages("devtools")

then call

library(devtools)
devtools::install_github("mitchest/optimus")

Contact

• Mitchell Lyons
• mitchell.lyons@gmail.com / mitchell.lyons@unsw.edu.au

References

Lyons et al. 2016. Model-based assessment of ecological community classifications. Journal of Vegetation Science: 27 (4) 704--715. DOI: http://dx.doi.org/10.1111/jvs.12400