R: An S4 class to represent a fitted TERGM by bootstrapped MPLE

btergm-class {btergm}

R Documentation

An S4 class to represent a fitted TERGM by bootstrapped MPLE

Description

An S4 class to represent a fitted TERGM by bootstrapped MPLE.

Show the coefficients of a btergm object.

Usage

## S4 method for signature 'btergm'
show(object)

## S4 method for signature 'btergm'
coef(object, invlogit = FALSE, ...)

## S4 method for signature 'btergm'
nobs(object)

btergm.se(object, print = FALSE)

## S4 method for signature 'btergm'
confint(object, parm, level = 0.95, type = "perc", invlogit = FALSE, ...)

timesteps.btergm(object)

## S4 method for signature 'btergm'
summary(object, level = 0.95, type = "perc", invlogit = FALSE, ...)

Arguments

`object`	A `btergm` object.
`invlogit`	Apply inverse logit transformation to the estimates and/or confidence intervals? That is, `\frac{1}{1 + \exp(-x)}`, where `x` is the respective value.
`...`	Further arguments to be passed through to the `confint` function.
`print`	Should the formatted coefficient table be printed to the R console along with significance stars (`print = TRUE`), or should the plain coefficient matrix be returned (`print = FALSE`)?
`parm`	Parameters (specified by integer position or character string).
`level`	The significance level for computation of the confidence intervals. The default is `0.95` (that is, an alpha value of 0.05). Other common values include `0.999`, `0.99`, and `0.9`.
`type`	Type of confidence interval, e.g., basic bootstrap interval (`type = "basic"`), percentile-based interval (`type = "perc"`, which is the default option), or bias-adjusted and accelerated confidence interval (`type = "bca"`). All options from the `type` argument of the boot.ci function in the boot package can be used to generate confidence intervals.

Details

btergm objects result from the estimation of a bootstrapped TERGM via the btergm function. 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.

Functions

coef(btergm): Return the coefficients of a btergm object.
nobs(btergm): Return the number of observations saved in a btergm object.
btergm.se(): Create a coefficient table from a btergm object

Create a coefficient matrix with standard errors and p-values.

This function can create a coefficient matrix with coefficients, standard errors, z-scores, and p-values, based on a fitted btergm object. 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@boot$t[, 1]) for the first model term in the btergm object called x).
confint(btergm): Return the confidence intervals for estimates in a btergm object.
timesteps.btergm(): Return the number of time steps saved in a btergm object.
summary(btergm): Summary of a fitted btergm object.

Slots

coef: Object of class "numeric". The coefficients.
boot: Object of class "matrix". The bootstrapping sample.
R: Object of class "numeric". Number of replications.
nobs: Object of class "numeric". Number of observations.
time.steps: Object of class "numeric". Number of time steps.
formula: Object of class "formula". The original model formula (without indices for the time steps).
formula2: The revised formula with the object references after applying the tergmprepare function.
response: Object of class "integer". The response variable.
effects: Object of class "data.frame". The effects that went into the glm call.
weights: Object of class "integer". The weights of the observations.
auto.adjust: Object of class "logical". Indicates whether automatic adjustment of dimensions was done before estimation.
offset: Object of class "logical". Indicates whether an offset matrix with structural zeros was used.
directed: Object of class "logical". Are the dependent networks directed?
bipartite: Object of class "logical". Are the dependent networks bipartite?
nvertices: Number of vertices.
data: The data after processing by the tergmprepare function.