`pca_centralities.Rd`

This function demonstrates ranks of centrality measures in order of information levels.

pca_centralities(x, scale.unit = TRUE, cut.off = 80, ncp = 5, graph = FALSE, axes = c(1, 2))

x | a list containg the computed centrality values |
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scale.unit | a boolean constant, whether data should be scaled to unit variance(default=TRUE) |

cut.off | The intensity that must be exceeded in cumulative percentage of variance of eigen values.(default=80) |

ncp | number of dimensions in final results (default=5) |

graph | a boolean constant, whether the graph shoul be displayed |

axes | a length 2 vector describing the number of components to plot(default=c(1,2)) |

a plot illustrating significant centralities in the order of contribution

This function represents centralities in the ranking list based on variable contribution to make principal components. PCA is a method for drawing out important variables from a data set. It helps user to reduced the dimensions in high dimensional data. It is more common to use for more than 3 dimensional datasets.

Husson, F., Lê, S., & Pagès, J. (2010). Exploratory Multivariate Analysis by Example using R. Chapman & Hall/CRC Computer Science & Data Analysis, 40(April), 240.

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