Convert an Observed Event Matrix to a Dichotomous matrix
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 itself
rank: 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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