faux.mesa.high: Goodreau's Faux Mesa High School as a network object

In ergm: Fit, Simulate and Diagnose Exponential-Family Models for Networks

Goodreau's Faux Mesa High School as a network object

Description

This data set (formerly called “fauxhigh”) represents a simulation of an in-school friendship network. The network is named faux.mesa.high because the school commnunity on which it is based is in the rural western US, with a student body that is largely Hispanic and Native American.

Usage

data(faux.mesa.high)

Format

faux.mesa.high is a network object with 205 vertices (students, in this case) and 203 undirected edges (mutual friendships). To obtain additional summary information about it, type summary(faux.mesa.high).

The vertex attributes are Grade, Sex, and Race. The Grade attribute has values 7 through 12, indicating each student's grade in school. The Race attribute is based on the answers to two questions, one on Hispanic identity and one on race, and takes six possible values: White (non-Hisp.), Black (non-Hisp.), Hispanic, Asian (non-Hisp.), Native American, and Other (non-Hisp.)

Licenses and Citation

If the source of the data set does not specified otherwise, this data set is protected by the Creative Commons License https://creativecommons.org/licenses/by-nc-nd/2.5/.

When publishing results obtained using this data set, the original authors (Resnick et al, 1997) should be cited. In addition this package should be cited as:

Mark S. Handcock, David R. Hunter, Carter T. Butts, Steven M. Goodreau, and Martina Morris. 2003 statnet: Software tools for the Statistical Modeling of Network Data
https://statnet.org.

Source

The data set is based upon a model fit to data from one school community from the AddHealth Study, Wave I (Resnick et al., 1997). It was constructed as follows:

A vector representing the sex of each student in the school was randomly re-ordered. The same was done with the students' response to questions on race and grade. These three attribute vectors were permuted independently. Missing values for each were randomly assigned with weights determined by the size of the attribute classes in the school.

The following ergm() specification was used to fit a model to the original data:

 ~ edges + nodefactor("Grade") + nodefactor("Race") +
nodefactor("Sex") + nodematch("Grade",diff=TRUE) +
nodematch("Race",diff=TRUE) + nodematch("Sex",diff=FALSE) +
gwdegree(1.0,fixed=TRUE) + gwesp(1.0,fixed=TRUE) + gwdsp(1.0,fixed=TRUE) 

The resulting model fit was then applied to a network with actors possessing the permuted attributes and with the same number of edges as in the original data.

The processes for handling missing data and defining the race attribute are described in Hunter, Goodreau & Handcock (2008).

References

Hunter D.R., Goodreau S.M. and Handcock M.S. (2008). Goodness of Fit of Social Network Models, Journal of the American Statistical Association.

Resnick M.D., Bearman, P.S., Blum R.W. et al. (1997). Protecting adolescents from harm. Findings from the National Longitudinal Study on Adolescent Health, Journal of the American Medical Association, 278: 823-32.

See Also

network, plot.network(), ergm(), faux.magnolia.high

ergm documentation built on Oct. 7, 2024, 5:08 p.m.