# template_network: Construct an Empty "Template" Network Consistent with an... In statnet/ergm.ego: Fit, Simulate and Diagnose Exponential-Family Random Graph Models to Egocentrically Sampled Network Data

## Description

Taking a egor object, constructs a network object with no edges whose vertices have the attributes of the egos in the dataset, replicating the egos as needed, and taking into accounts their sampling weights.

## Usage

 1 template_network(x, N, scaling = c("round", "sample"), ...) 

## Arguments

 x A egor object or a data.frame. N The target number of vertices the output network should have. scaling If egor contains weights or N is not a multiple of number of egos in the sample, it may not be possible, for a finite N to represent each ego exactly according to its relative weight, and scaling controls how the fractional egos are allocated: "round"(the default) Rather than treating N as a hard setting, calculate N w_i / w_\cdot for each ego i and round it to the nearest integer. Then, the N actually used will be the sum of these rounded freqencies. "sample"Resample in proportion to w_i. ... Additional arguments, currently unused.

## Value

A network object.

## Author(s)

Pavel N. Krivitsky

as.egor.network, which performs the inverse operation.
  1 2 3 4 5 6 7 8 9 10 11 12 data(faux.mesa.high) summary(faux.mesa.high, print.adj = FALSE) fmh.ego <- as.egor(faux.mesa.high) # Same actor attributes fmh.template <- template_network(fmh.ego, N=network.size(faux.mesa.high)) summary(fmh.template, print.adj = FALSE) # Twice the actors, same distribution fmh2.template <- template_network(fmh.ego, N=2*network.size(faux.mesa.high)) summary(fmh2.template, print.adj = FALSE)