Summary
The micompr R package implements a procedure for comparing multivariate samples associated with different groups. The procedure uses principal component analysis to convert multivariate observations into a set of linearly uncorrelated statistical measures, which are then compared using a number of statistical methods. This technique is independent of the distributional properties of samples and automatically selects features that best explain their differences, avoiding manual selection of specific points or summary statistics. The procedure is appropriate for comparing samples of time series, images, spectrometric measures or similar multivariate observations.
How to install
Install the development version from GitHub with the following command (requires the devtools package):
devtools::install_github("fakenmc/micompr")
The package has been submitted to CRAN and, if accepted, will be installable via the following instruction:
install.packages("micompr")
Documentation
All methods and functions are fully documented and can be queried using the built-in help system. After installation, to access the man pages, invoke the micompr help page as follows:
help("micompr")
Additionally, the package contains two vignettes with a number of examples.
References
Practice
- Fachada N, Rodrigues J, Lopes VV, Martins RC, Rosa AC. (2016) micompr: An R
Package for Multivariate Independent Comparison of Observations. Under peer-review. (arXiv version available at http://arxiv.org/abs/1603.06907)
Theory
- Fachada N, Lopes VV, Martins RC, Rosa AC. (2016) Model-independent
comparison of simulation output. Under peer-review. (arXiv version available at http://arxiv.org/abs/1509.09174)