summary.ergm {ergm}

Summarizing ERGM Model Fits

Description

base::summary() method for ergm() fits.

Usage

## S3 method for class 'ergm'
summary(
  object,
  ...,
  correlation = FALSE,
  covariance = FALSE,
  total.variation = TRUE
)

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

Arguments

object

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

...

For summary.ergm() additional arguments are passed to logLik.ergm(). For print.summary.ergm(), to stats::printCoefmat().

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.

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 variation is used. The p-values are based on this source of variation.

x

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

digits

significant digits for coefficients

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.formula, print.fitinfo, print.coefmat, print.message, print.deviances, print.drop, print.offset, print.call

which components of the fit summary to print.

Details

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

The default printout of the summary object contains the call, number of iterations used, null and residual deviances, and the values of AIC and BIC (and their MCMC standard errors, if applicable). The coefficient table contains the following columns:

• Estimate, ⁠Std. Error⁠ - parameter estimates and their standard errors

• ⁠MCMC %⁠ - if total.variation=TRUE (default) the percentage of standard error attributable to MCMC estimation process rounded to an integer. See also vcov.ergm() and its sources argument.

• ⁠z value⁠, ⁠Pr(>|z|)⁠ - z-test and p-values

Value

The returned object is a list of class "ergm.summary" with the following elements:

formula

ERGM model formula

call

R call used to fit the model

correlation, covariance

whether to print correlation/covariance matrices of the estimated parameters

pseudolikelihood

was the model estimated with MPLE

independence

is the model dyad-independent

control

the control.ergm() object used

samplesize

MCMC sample size

message

optional message on the validity of the standard error estimates

null.lik.0

It is TRUE of the null model likelihood has not been calculated. See logLikNull()

devtext, devtable

Deviance type and table

aic, bic

values of AIC and BIC

coefficients

matrices with model parameters and associated statistics

asycov

asymptotic covariance matrix

asyse

asymptotic standard error matrix

offset, drop, estimate, iterations, mle.lik, null.lik

see documentation of the object returned by ergm()

See Also

The model fitting function ergm(), print.ergm(), and base::summary(). Function stats::coef() will extract the matrix of coefficients with standard errors, t-statistics and p-values.

Examples

 data(florentine)

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