ergm_tidiers {broom} | R Documentation |
These methods tidy the coefficients of an exponential random graph model estimated with the ergm package into a summary, and construct a one-row glance of the model's statistics. 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, quick = FALSE, ...) ## S3 method for class 'ergm' glance(x, deviance = FALSE, mcmc = FALSE, ...)
x |
an ergm object |
conf.int |
whether to include a confidence interval |
conf.level |
confidence level of the interval, used only if
|
exponentiate |
whether to exponentiate the coefficient estimates and confidence intervals |
quick |
whether to compute a smaller and faster version, containing
only the |
... |
extra arguments passed to |
deviance |
whether to report null and residual deviance for the model,
along with degrees of freedom; defaults to |
mcmc |
whether to report MCMC interval, burn-in and sample size used to
estimate the model; defaults to |
There is no augment
method for ergm objects.
All tidying methods return a data.frame
without rownames.
The structure depends on the method chosen.
tidy.ergm
returns one row for each coefficient, with five 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 |
If conf.int=TRUE
, it also includes columns for conf.low
and
conf.high
.
glance.ergm
returns a one-row data.frame with the columns
independence |
Whether the model assumed dyadic independence |
iterations |
The number of iterations performed before convergence |
logLik |
If applicable, the log-likelihood associated with the model |
AIC |
The Akaike Information Criterion |
BIC |
The Bayesian Information Criterion |
If deviance=TRUE
, and if the model supports it, the
data frame will also contain the columns
null.deviance |
The null deviance of the model |
df.null |
The degrees of freedom of the null deviance |
residual.deviance |
The residual deviance of the model |
df.residual |
The degrees of freedom of the residual deviance |
Last, if mcmc=TRUE
, the data frame will also contain
the columns
MCMC.interval |
The interval used during MCMC estimation |
MCMC.burnin |
The burn-in period of the MCMC estimation |
MCMC.samplesize |
The sample size used during MCMC estimation |
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). http://www.jstatsoft.org/v24/i03/.
ergm
,
control.ergm
,
summary.ergm
if (require("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) }