Tidy a(n) ergm object
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.
The methods should work with any model that conforms to the ergm class, such as those produced from weighted networks by the ergm.count package.
## S3 method for class 'ergm' tidy(x, conf.int = FALSE, conf.level = 0.95, exponentiate = FALSE, ...)
x |
An |
conf.int |
Logical indicating whether or not to include a confidence
interval in the tidied output. Defaults to |
conf.level |
The confidence level to use for the confidence interval
if |
exponentiate |
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 |
... |
Additional arguments to pass to |
A tibble::tibble with one row for each coefficient in the exponential random graph model, with columns:
term |
The term in the model being estimated and tested |
estimate |
The estimated coefficient |
std.error |
The standard error |
mcmc.error |
The MCMC error |
p.value |
The two-sided p-value |
Hunter DR, Handcock MS, Butts CT, Goodreau SM, Morris M (2008b). ergm: A Package to Fit, Simulate and Diagnose Exponential-Family Models for Networks. Journal of Statistical Software, 24(3). https://www.jstatsoft.org/v24/i03/.
Other ergm tidiers:
glance.ergm()
if (requireNamespace("ergm", quietly = TRUE)) {
library(ergm)
# Using the same example as the ergm package
# Load the Florentine marriage network data
data(florentine)
# Fit a model where the propensity to form ties between
# families depends on the absolute difference in wealth
gest <- ergm(flomarriage ~ edges + absdiff("wealth"))
# Show terms, coefficient estimates and errors
tidy(gest)
# Show coefficients as odds ratios with a 99% CI
tidy(gest, exponentiate = TRUE, conf.int = TRUE, conf.level = 0.99)
# Take a look at likelihood measures and other
# control parameters used during MCMC estimation
glance(gest)
glance(gest, deviance = TRUE)
glance(gest, mcmc = TRUE)
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