R: Tidying method for a bootstrapped temporal exponential random...

btergm_tidiers {broom} | R Documentation

## Tidying method for a bootstrapped temporal exponential random graph model

### Description

This method tidies the coefficients of a bootstrapped temporal exponential random graph model estimated with the xergm. It simply returns the coefficients and their confidence intervals.

### Usage

    ## S3 method for class 'btergm'
    tidy(x, conf.level = 0.95, exponentiate = FALSE,
      quick = FALSE, ...)

### Arguments

`x` | a `btergm` object
`conf.level` | confidence level of the bootstrapped interval
`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 (currently not used)

### Details

There is no `augment` or `glance` method for ergm objects.

### Value

A `data.frame` without rownames.

`tidy.btergm` returns one row for each coefficient, with four columns:

`term` | The term in the model being estimated and tested
`estimate` | The estimated coefficient
`conf.low` | The lower bound of the confidence interval
`conf.high` | The lower bound of the confidence interval

### See Also

`btergm`

### Examples

    if (require("xergm")) {
        # Using the same simulated example as the xergm package
        # Create 10 random networks with 10 actors
        networks <- list()
        for(i in 1:10){
            mat <- matrix(rbinom(100, 1, .25), nrow = 10, ncol = 10)
            diag(mat) <- 0
            nw <- network::network(mat)
            networks[[i]] <- nw
        }
        # Create 10 matrices as covariates
        covariates <- list()
        for (i in 1:10) {
            mat <- matrix(rnorm(100), nrow = 10, ncol = 10)
            covariates[[i]] <- mat
        }
        # Fit a model where the propensity to form ties depends
        # on the edge covariates, controlling for the number of
        # in-stars
        btfit <- btergm(networks ~ edges + istar(2) +
                          edgecov(covariates), R = 100)

        # Show terms, coefficient estimates and errors
        tidy(btfit)

        # Show coefficients as odds ratios with a 99% CI
        tidy(btfit, exponentiate = TRUE, conf.level = 0.99)
    }

[Package broom version 0.4.2 Index]