Description Usage Arguments Details Value References See Also Examples

Ranks the p features in an n by p design matrix where n represents the sample size and p is the number of features.

1 | ```
cosci_is(dat, min.alpha, small.perturbation = 10^(-6))
``` |

`dat` |
n by p data matrix |

`min.alpha` |
the smallest threshold (typically set to 0) |

`small.perturbation` |
a small positive number to remove ties. Default value is 10^(-6) |

Uses the univariate merging algorithm `bmt`

and produces a score
for each feature that reflects its relative importance for clustering.

a p vector of scores

Banerjee, T., Mukherjee, G. and Radchenko P., Feature Screening in Large Scale Cluster Analysis, Journal of Multivariate Analysis, Volume 161, 2017, Pages 191-212

P. Radchenko, G. Mukherjee, Convex clustering via l1 fusion penalization, J. Roy. Statist, Soc. Ser. B (Statistical Methodology) (2017) doi:10.1111/rssb.12226.

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