Tidy summarizes information about the components of a model. A model component might be a single term in a regression, a single hypothesis, a cluster, or a class. Exactly what tidy considers to be a model component varies across models but is usually self-evident. If a model has several distinct types of components, you will need to specify which components to return.

This method tidies the coefficients of a bootstrapped temporal exponential random graph model estimated with the xergm. It simply returns the coefficients and their confidence intervals.

# S3 method for btergm
tidy(x, conf.level = 0.95, exponentiate = FALSE, ...)

## Arguments

x A btergm::btergm() object. Confidence level for confidence intervals. Defaults to 0.95. Logical indicating whether or not to exponentiate the the coefficient estimates. This is typical for logistic and multinomial regressions, but a bad idea if there is no log or logit link. Defaults to FALSE. Additional arguments. Not used. Needed to match generic signature only. Cautionary note: Misspelled arguments will be absorbed in ..., where they will be ignored. If the misspelled argument has a default value, the default value will be used. For example, if you pass conf.lvel = 0.9, all computation will proceed using conf.level = 0.95. Additionally, if you pass newdata = my_tibble to an augment() method that does not accept a newdata argument, it will use the default value for the data argument.

tidy(), btergm::btergm()

## Value

A tibble::tibble() with columns:

conf.high

Upper bound on the confidence interval for the estimate.

conf.low

Lower bound on the confidence interval for the estimate.

estimate

The estimated value of the regression term.

term

The name of the regression term.

## Examples


#> Version 1.16.0 created on 2019-11-30.
#> copyright (c) 2005, Carter T. Butts, University of California-Irvine
#>                     Mark S. Handcock, University of California -- Los Angeles
#>                     David R. Hunter, Penn State University
#>                     Martina Morris, University of Washington
#>                     Skye Bender-deMoll, University of Washington
#>  For citation information, type citation("network").
#>  Type help("network-package") to get started.#>
#> ergm: version 3.10.4, created on 2019-06-10
#> Copyright (c) 2019, Mark S. Handcock, University of California -- Los Angeles
#>                     David R. Hunter, Penn State University
#>                     Carter T. Butts, University of California -- Irvine
#>                     Steven M. Goodreau, University of Washington
#>                     Pavel N. Krivitsky, University of Wollongong
#>                     Martina Morris, University of Washington
#>                     with contributions from
#>                     Li Wang
#>                     Kirk Li, University of Washington
#>                     Skye Bender-deMoll, University of Washington
#> Based on "statnet" project software (statnet.org).
#> or type citation("ergm").#> NOTE: Versions before 3.6.1 had a bug in the implementation of the bd()
#> constriant which distorted the sampled distribution somewhat. In
#> NEWS and the documentation for more details.#> NOTE: Some common term arguments pertaining to vertex attribute and
#> level selection have changed in 3.10.0. See terms help for more
#> details. Use ‘options(ergm.term=list(version="3.9.4"))’ to use old
#> behavior.#>
#> Attaching package: ‘xergm.common’#> The following object is masked from ‘package:ergm’:
#>
#>     gof#> Error: package or namespace load failed for ‘btergm’:
#>   call: fun(libname, pkgname)
#>   error: X11 library is missing: install XQuartz from xquartz.macosforge.orgset.seed(1)

# Create 10 random networks with 10 actors

networks <- list()

for (i in 1:10) {
mat <- matrix(rbinom(100, 1, .25), nrow = 10, ncol = 10)
diag(mat) <- 0
nw <- network::network(mat)
networks[[i]] <- nw
}

# Create 10 matrices as covariates

covariates <- list()

for (i in 1:10) {
mat <- matrix(rnorm(100), nrow = 10, ncol = 10)
covariates[[i]] <- mat
}

# Fit a model where the propensity to form ties depends
# on the edge covariates, controlling for the number of
# in-stars
btfit <- btergm(networks ~ edges + istar(2) + edgecov(covariates), R = 100)#> Error in btergm(networks ~ edges + istar(2) + edgecov(covariates), R = 100): could not find function "btergm"
# Show terms, coefficient estimates and errors