R: Univariate Conditional Uniform Graph Tests

cug.test {sna} | R Documentation

## Univariate Conditional Uniform Graph Tests

### Description

`cug.test` takes an input network and conducts a conditional uniform graph (CUG) test of the statistic in `FUN`, using the conditioning statistics in `cmode`. The resulting test object has custom print and plot methods.

### Usage

    cug.test(dat, FUN, mode = c("digraph", "graph"), cmode = c("size",
        "edges", "dyad.census"), diag = FALSE, reps = 1000,
        ignore.eval = TRUE, FUN.args = list())

### Arguments

`dat` | one or more input graphs.
`FUN` | the function generating the test statistic; note that this must take a graph as its first argument, and return a single numerical value.
`mode` | `graph` if `dat` is an undirected graph, else `digraph`.
`cmode` | string indicating the type of conditioning to be applied.
`diag` | logical; should self-ties be treated as valid data?
`reps` | number of Monte Carlo replications to use.
`ignore.eval` | logical; should edge values be ignored? (Note: `TRUE` is usually more efficient.)
`FUN.args` | a list containing any additional arguments to `FUN`.

### Details

`cug.test` is an improved version of `cugtest`, for use only with univariate CUG hypotheses. Depending on `cmode`, conditioning on the realized size, edge count (or exact edge value distribution), or dyad census (or dyad value distribution) can be selected. Edges are treated as unvalued unless `ignore.eval=FALSE`; since the latter setting is less efficient for sparse graphs, it should be used only when necessary.

A brief summary of the theory and goals of conditional uniform graph testing can be found in the reference below. See also `cugtest` for a somewhat informal description.

### Value

An object of class `cug.test`.

### Author(s)

Carter T. Butts buttsc@uci.edu

### References

Butts, Carter T. (2008). “Social Networks: A Methodological Introduction.” Asian Journal of Social Psychology, 11(1), 13–41.

### See Also

`cugtest`

### Examples

    #Draw a highly reciprocal network
    g<-rguman(1,15,mut=0.25,asym=0.05,null=0.7)

    #Test transitivity against size, density, and the dyad census
    cug.test(g,gtrans,cmode="size")
    cug.test(g,gtrans,cmode="edges")
    cug.test(g,gtrans,cmode="dyad.census")

[Package sna version 2.7 Index]