R: Tidying methods for an exponential random graph model

ergm_tidiers {broom} | R Documentation

## Tidying methods for an exponential random graph model

### Description

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.

### Usage

    ## 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, ...)

### Arguments

`x` | an ergm object
`conf.int` | whether to include a confidence interval
`conf.level` | confidence level of the interval, used only if `conf.int=TRUE`
`exponentiate` | whether to exponentiate the coefficient estimates and confidence intervals
`quick` | whether to compute a smaller and faster version, containing only the `term` and `estimate` columns.
`...` | extra arguments passed to `summary.ergm`
`deviance` | whether to report null and residual deviance for the model, along with degrees of freedom; defaults to `FALSE`
`mcmc` | whether to report MCMC interval, burn-in and sample size used to estimate the model; defaults to `FALSE`

### Details

There is no `augment` method for ergm objects.

### Value

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

### References

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/.

### See Also

`ergm`, `control.ergm`, `summary.ergm`

### Examples

    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)
    }

[Package broom version 0.4.2 Index]