event2dichot | R Documentation |

Given one or more valued adjacency matrices (possibly derived from observed interaction “events”), `event2dichot`

returns dichotomized equivalents.

event2dichot(m, method="quantile", thresh=0.5, leq=FALSE)

`m` |
one or more (valued) input graphs. |

`method` |
one of “quantile,” “rquantile,” “cquantile,” “mean,” “rmean,” “cmean,” “absolute,” “rank,” “rrank,” or “crank”. |

`thresh` |
dichotomization thresholds for ranks or quantiles. |

`leq` |
boolean indicating whether values less than or equal to the threshold should be taken as existing edges; the alternative is to use values strictly greater than the threshold. |

The methods used for choosing dichotomization thresholds are as follows:

quantile: specified quantile over the distribution of all edge values

rquantile: specified quantile by row

cquantile: specified quantile by column

mean: grand mean

rmean: row mean

cmean: column mean

absolute: the value of

`thresh`

itselfrank: specified rank over the distribution of all edge values

rrank: specified rank by row

crank: specified rank by column

Note that when a quantile, rank, or value is said to be “specified,” this refers to the value of `thresh`

.

The dichotomized data matrix (or matrices)

Carter T. Butts buttsc@uci.edu

Wasserman, S. and Faust, K. (1994). *Social Network Analysis: Methods and Applications.* Cambridge: Cambridge University Press.

#Draw a matrix of normal values n<-matrix(rnorm(25),nrow=5,ncol=5) #Dichotomize by the mean value event2dichot(n,"mean") #Dichotomize by the 0.95 quantile event2dichot(n,"quantile",0.95)

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