"btergm"btergm objects result from the estimation of a bootstrapped TERGM via the btergm function in the btergm objects contain the coefficients, the bootstrapping samples of the coefficients, the number of replications, the number of observations, the number of time steps, the original formula, and the response, effects and weights objects that were fed into the glm call for estimating the model.## S3 method for class 'btergm':
summary(object, level = 0.95, ...)## S3 method for class 'btergm':
show(object)
## S3 method for class 'btergm':
nobs(object)
## S3 method for class 'btergm':
coef(object, ...)
## S3 method for class 'btergm':
confint(object, parm, level = 0.95, ...)
btergm.se(object, print = FALSE)
btergm.timesteps(object)
btergm object.0.95 (that is, an alpha value of 0.05). Other common values include 0.999, 0.99, 0.9, and 0.5.print = TRUE), or should the plain coefficient matrix be returned (print = FALSE)?btergm objects: The coef and show methods return the coefficients; the summary method gives a model summary. The nobs method returns the number of observations. The {confint} method returns confidence intervals from the bootstrap replications of btergm objects, and the user can specify the confidence level. The method returns a matrix with three columns: the estimate, the lower bound, and the upper bound of the confidence interval for each model term.The btergm.se function computes standard errors and p values for btergm objects. It returns a matrix with four columns: the estimate, the standard error, the z value, and the p value for each model term. If the argument print = TRUE is used, the matrix is printed to the R console as a formatted coefficient matrix with significance stars instead. Note that confidence intervals are the preferred way of interpretation for bootstrapped TERGMs; standard errors are only accurate if the bootstrapped data are normally distributed, which is not always the case. Various methods for checking for normality for each model term are available, for example quantile-quantile plots (e.g., qqnorm(x@bootsamp[, 1]) for the first model term in the btergm object called x).
The btergm.timesteps function extracts the number of time steps from a btergm object. The number of time steps is the number of networks being modeled on the left-hand side of the model formula.
Desmarais, Bruce A. and Skyler J. Cranmer (2012):
Statistical Mechanics of Networks: Estimation and Uncertainty.
Physica A 391: 1865--1876.
Desmarais, Bruce A. and Skyler J. Cranmer (2010): Consistent Confidence Intervals for Maximum Pseudolikelihood Estimators. Neural Information Processing Systems 2010 Workshop on Computational Social Science and the Wisdom of Crowds.