R: Convert an Observed Event Matrix to a Dichotomous matrix

event2dichot {sna} | R Documentation

## Convert an Observed Event Matrix to a Dichotomous matrix

### Description

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

### Usage

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

### Arguments

`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.

### Details

The methods used for choosing dichotomization thresholds are as follows:

1. quantile: specified quantile over the distribution of all edge values

2. rquantile: specified quantile by row

3. cquantile: specified quantile by column

4. mean: grand mean

5. rmean: row mean

6. cmean: column mean

7. absolute: the value of `thresh` itself

8. rank: specified rank over the distribution of all edge values

9. rrank: specified rank by row

10. 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`.

### Value

The dichotomized data matrix (or matrices)

### Author(s)

Carter T. Butts buttsc@uci.edu

### References

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

### Examples

    #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)

[Package sna version 2.5 Index]