R: Summarizing ERGM Model Fits

summary.ergm {ergm} | R Documentation

## 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 function `summary.ergm()` computes and returns a list of summary statistics of the fitted `ergm()` model given in `object`. Note that for backwards compatibility, it returns two coefficient tables: `$coefs` which does not contain the z-statistics and `$coefficeints` which does (and is therefore more similar to those returned by `stats::summary.lm()`).

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
`coefs, coefficients` | data frames 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 data frame of coefficients with standard errors, t-statistics and p-values.

### Examples

     data(florentine)

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

[Package ergm version 4.1.2 Index]