| runDS {RcmdrPlugin.depthTools} | R Documentation |
Distance to the Trimmed Mean Classification Method
Description
Implementation of the classification technique based on assigning each observation to the group that minimizes the distance of the observation to the trimmed mean of the group.
Details
The user can choose the learning and test sets, as well as the labels corresponding to the learning set. The DS method proceeds by first computing the alpha trimmed mean corresponding to each group from the learning set, then computing the distance from a new observation to each trimmed mean. The new sample will then be assigned to the group that minimizes such distance. At the moment, only the Euclidean distance is implemented. The predicted labels can be stored as a vector.
Author(s)
Sara Lopez-Pintado sl2929@columbia.edu and
Aurora Torrente etorrent@est-econ.uc3m.es
References
Lopez-Pintado, S. et al. (2010). Robust depth-based tools for the analysis of gene expression data. Biostatistics, 11 (2), 254-264.
See Also
computeTmean, runTAD