micompr
Multivariate Independent Comparison of Observations
A procedure for comparing multivariate samples associated with
different groups. It 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. The
procedure 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. It is appropriate
for comparing samples of time series, images, spectrometric measures or
similar multivariate observations. This package is described in Fachada et
al. (2016)
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("nunofachada/micompr")
A stable version of the package is available on CRAN and can be installed with 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. The R Journal 8(2):405–420. https://doi.org/10.32614/RJ-2016-055
Theory
- Fachada N, Lopes VV, Martins RC, Rosa AC. (2017) Model-independent comparison of simulation output. Simulation Modelling Practice and Theory. 72:131–149. https://doi.org/10.1016/j.simpat.2016.12.013 (arXiv preprint)