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

In btergm: Temporal Exponential Random Graph Models by Bootstrapped Pseudolikelihood

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.

See Also

Other tergm-classes: createBtergm(), createMtergm(), createTbergm(), mtergm-class, tbergm-class

btergm documentation built on May 29, 2024, 12:09 p.m.