# event2dichot: Convert an Observed Event Matrix to a Dichotomous matrix In sna: Tools for Social Network Analysis

 event2dichot 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)

```

sna documentation built on June 1, 2022, 9:06 a.m.