ergm (version 3.9.4)

print.summary.ergm: Summarizing ERGM Model Fits

Description

summary method for ergm fits.

Usage

# S3 method for summary.ergm
print(x, digits = max(3, getOption("digits") - 3),
  correlation = FALSE, covariance = FALSE,
  signif.stars = getOption("show.signif.stars"), eps.Pvalue = 1e-04,
  print.header = TRUE, print.formula = TRUE, print.fitinfo = TRUE,
  print.coefmat = TRUE, print.message = TRUE, print.deviances = TRUE,
  print.drop = TRUE, print.offset = TRUE, print.degeneracy = TRUE,
  ...)
# S3 method for ergm
summary(object, ..., correlation = FALSE,
  covariance = FALSE, total.variation = TRUE)

Arguments

x

object of class summary.ergm returned by summary.ergm().

digits

Significant digits for coefficients

correlation

logical; if TRUE, the correlation matrix of the estimated parameters is returned and printed.

covariance

logical; if TRUE, the covariance matrix of the estimated parameters is returned and printed.

signif.stars

whether to print dots and stars to signify statistical significance. See print.summary.lm().

eps.Pvalue

\(p\)-values below this level will be printed as "<eps.Pvalue".

print.header, print.formula, print.fitinfo, print.coefmat, print.message, print.deviances, print.drop, print.offset, print.degeneracy

which components of the fit summary to print.

…

Arguments to logLik.ergm

object

an object of class "ergm", usually, a result of a call to ergm.

total.variation

logical; if TRUE, the standard errors reported in the Std. Error column are based on the sum of the likelihood variation and the MCMC variation. If FALSE only the likelihood varuation is used. The \(p\)-values are based on this source of variation.

Value

The function summary.ergm computes and returns a list of summary statistics of the fitted ergm model given in object.

Details

summary.ergm tries to be smart about formatting the coefficients, standard errors, etc.

See Also

network, ergm, print.ergm. The model fitting function ergm, summary.

Function coef will extract the matrix of coefficients with standard errors, t-statistics and p-values.

Examples

# NOT RUN {
 data(florentine)

 x <- ergm(flomarriage ~ density)
 summary(x)

# }