## statnetWeb Introduction

`statnetWeb`

is a web-based interface that provides access
to cross-sectional network modeling with the `statnet`

packages `network`

, `sna`

and `ergm`

.
Today we are using this app for the morning sessions on ERGMs.

## Getting Started

Open Rstudio and load `statnetWeb`

:

`library(statnetWeb)`

Launch the Shiny app:

`run_sw()`

## The Lab Assignment

You are going to execute a typical ERGM workflow: fit and assess
three models sequentially, using the `faux.mesa.high`

built
in network data.

### Start with the `edges`

only model

on the Data Tab: select the `faux.mesa.high`

network
data

on the Fit Model tab:

- Add the edges term
- Fit the model
- Save the model
- Interpret the estimated coefficient: calculate the density of the
network as a function of the coefficient.

on the MCMC Diagnostics tab

- can you run the dx for this model?
- why or why not?

on the Goodness of Fit tab:

- run the default goodness of fit dx
- interpret the results

### Then: repeat these steps, for the following models:

- Add Terms:
`nodefactor("Grade") + nodefactor("Race") + nodefactor("Sex")`

- Add Terms:
`triangle`

### Questions

- For the model with the nodefactor terms added
- interpret each of the coefficients: Significance? Direction? Size?
Compared to the other terms?
- compare the goodness of fit to the
`edges`

only model:
which higher order stats look better? which do not?

- For the model with the triangle term added
- use your breakout group Slack channel to report your findings
- discuss

*Last updated:* 2022-07-07 with statnetWeb v0.5.6