Principal Components Analysis (PCA) is a common way to study the sources of variation in a high-dimensional data set. Typically, the leading principal components are used to understand the variation in the data or to reduce the dimension of the data for subsequent analysis. The remaining principal components are ignored since they explain little of the variation in the data. However, the space spanned by the low variation principal components may contain interesting structure, structure that PCA cannot find. Prinsimp is an R package that looks for interesting structure of low variability. “Interesting” is defined in terms of a simplicity measure. Looking for interpretable structure in a low variability space has particular importance in evolutionary biology, where such structure can signify the existence of a genetic constraint.

Jonathan Zhang , Nancy Heckman , Davor Cubranic , Joel G. Kingsolver , Travis Gaydos , J.S. Marron

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For attribution, please cite this work as

Zhang, et al., "The R Journal: Prinsimp", The R Journal, 2014

BibTeX citation

  author = {Zhang, Jonathan and Heckman, Nancy and Cubranic, Davor and Kingsolver, Joel G. and Gaydos, Travis and Marron, J.S.},
  title = {The R Journal: Prinsimp},
  journal = {The R Journal},
  year = {2014},
  note = {},
  doi = {10.32614/RJ-2014-022},
  volume = {6},
  issue = {2},
  issn = {2073-4859},
  pages = {27-42}