# mixingmatrix.egor: Summarizing the mixing among groups in an egocentric dataset In statnet/ergm.ego: Fit, Simulate and Diagnose Exponential-Family Random Graph Models to Egocentrically Sampled Network Data

## Description

A `mixingmatrix` method for `egor` objects, to return counts of how often a ego of each group nominates an alter of each group.

## Usage

 ```1 2``` ```## S3 method for class 'egor' mixingmatrix(object, attrname, rowprob = FALSE, weight = TRUE, ...) ```

## Arguments

 `object` A `egor` object. `attrname` A character vector containing the name of the network attribute whose mixing matrix is wanted. `rowprob` Whether the counts should be normalized by row sums. That is, whether they should be proportions conditional on the ego's group. `weight` Whether sampling weights should be incorporated into the calculation (`TRUE`, the default) or ignored (`FALSE`). `...` Additional arguments, currently unused.

## Value

A matrix with a row and a column for each level of `attrname`.

Note that, unlike `mixingmatrix`, what is counted are nominations, not ties. This means that under an egocentric census, the diagonal of `mixingmatrix.egor` will be twice that returned by `mixingmatrix` for the original undirected network.

`mixingmatrix`, `nodemix`, `summary` method for egocentric data
 ```1 2 3 4 5 6 7 8``` ```data(faux.mesa.high) fmh.ego <- as.egor(faux.mesa.high) (mm <- mixingmatrix(faux.mesa.high,"Grade")) (mm.ego <- mixingmatrix(fmh.ego,"Grade")) stopifnot(isTRUE(all.equal({tmp<-unclass(mm\$matrix); diag(tmp) <- diag(tmp)*2; tmp}, mm.ego, check.attributes=FALSE))) ```