marginalplot: Plot marginal effects for two-way interactions in (T)ERGMs

In btergm: Temporal Exponential Random Graph Models by Bootstrapped Pseudolikelihood

Plot marginal effects for two-way interactions in (T)ERGMs

Description

Plot marginal effects for two-way interactions in (T)ERGMs.

Usage

marginalplot(
  model,
  var1,
  var2,
  inter,
  ci = 0.95,
  rug = FALSE,
  point = FALSE,
  structzeromat = NULL,
  zeroline = TRUE,
  color = "black",
  xlab = NULL,
  ylab = NULL
)

Arguments

model	An ergm object as generated by the ergm package. Note that marginal effects plots cannot be created for btergm objects because the variance-covariance matrix is not valid. However, it should be possible to apply the marginalplot function to MCMC-MLE-estimated TERGMs because the ergm object is stored in the ergm slot of an mtergm object. To do this, supply the ergm object instead of the mtergm object (e.g., marginalplot(mtergmobject@ergm)).
var1	Name of the first main variable. This is the focal variable.
var2	Name of the second main variable. This is the conditioning variable.
inter	Name of the interaction effect.
ci	Significance level.
rug	Display the distribution of the conditioning variable at the bottom of the plot?
point	Display error bars for the levels of the conditioning variable (instead of a continuous curve)?
structzeromat	An optional matrix object which indicates dyads that should be deleted prior to the calculation of the confidence interval for the marginal effect curve. This is useful when such a matrix was used to indicate structural zeros during estimation. In this event, the dyads characterized by structural zeros are not allowed to be tied, therefore they should be removed from the set of dyads used for the calculation of marginal effects. The matrix should contain ones for structural zeros and zeros for entries that should be used.
zeroline	Draw a horizontal line to indicate zero for the first main variable?
color	Color of the curve, confidence interval, and distribution.
xlab	Axis label for the second (conditioning) variable.
ylab	Axis label for the first (focal) variable.

Details

The marginalplot function creates marginal effects plots for ERGMs with interaction effects. The user has to supply the ergm object and the coefficient names of the first main variable, the second main variable, and the interaction term as stored in the coefficients vector inside the ergm object. It is possible to draw continuous curves or discrete error bars depending on the nature of the data (using the point argument). The distribution of the second (conditioning) variable can be plotted at the bottom of the viewport using the rug argument.

The resulting marginal effects plot is a ggplot2 plot. This means it can be extended by plotting additional elements and using themes.

See Also

Other interpretation: edgeprob(), interpret()

Examples

## Not run: 
# data preparation
data("florentine")
n <- network.size(flobusiness)
wealth <- get.vertex.attribute(flobusiness, "wealth")
priorates <- get.vertex.attribute(flobusiness, "priorates")
wealth.icov <- matrix(rep(wealth, n), ncol = n, byrow = TRUE)
priorates.icov <- matrix(rep(priorates, n), ncol = n, byrow = TRUE)
interac <- wealth.icov * priorates.icov

# estimate model with interaction effect
model <- ergm(flobusiness ~ edges + esp(1) + edgecov(wealth.icov)
                + edgecov(priorates.icov) + edgecov(interac))

# plot the interaction (note the additional optional ggplot2 elements)
marginalplot(model, var1 = "edgecov.wealth.icov",
             var2 = "edgecov.priorates.icov", inter = "edgecov.interac",
             color = "darkred", rug = TRUE, point = FALSE,
             xlab = "Priorates", ylab = "Wealth") +
  ggplot2::theme_bw() +
  ggplot2::ggtitle("Interaction effect")

## End(Not run)

btergm documentation built on Oct. 6, 2023, 1:07 a.m.