lame (version 1.3.4)

ab_plot: Visualize sender and receiver random effects

Description

Creates a visualization of the additive sender (row) and receiver (column) random effects from an AME or LAME model. Automatically detects whether effects are static or dynamic and provides appropriate visualization options.

Usage

ab_plot(
  fit,
  effect = c("sender", "receiver"),
  sorted = TRUE,
  labels = NULL,
  title = NULL,
  time_point = NULL,
  plot_type = c("snapshot", "trajectory", "faceted", "ribbon"),
  show_actors = NULL
)

Value

A ggplot2 object that can be further customized

Arguments

fit

An object of class "ame" or "lame" from fitting an AME model

effect

Character string specifying which effect to plot: "sender" (default) or "receiver"

sorted

Logical; if TRUE (default), actors are sorted by effect magnitude. Applies to ame / lame fits; ame_als fits are always sorted by value.

labels

Logical; if TRUE, actor labels are shown on x-axis (default TRUE for n <= 50 actors). Applies to ame / lame fits.

title

Optional title for the plot (ame / lame fits).

time_point

For dynamic effects, which time point to plot (default: last). Can be a numeric index, "all" for a faceted plot, or "average" for time-averaged

plot_type

For dynamic effects: "snapshot" (single time), "trajectory" (evolution over time), "faceted" (grid of time points), or "ribbon" (effect path with a 95\ band per period). For static effects, this parameter is ignored.

show_actors

Character vector of specific actors to highlight (for dynamic trajectory / ribbon plots)

Author

Cassy Dorff, Shahryar Minhas, Tosin Salau

Details

The additive effects in AME models represent:

Sender effects (a)

Actor-specific tendencies to form outgoing ties. Positive values indicate actors who send more ties than expected; negative values indicate actors who send fewer ties.

Receiver effects (b)

Actor-specific tendencies to receive incoming ties. Positive values indicate actors who receive more ties than expected; negative values indicate actors who receive fewer ties.

For static effects, the plot displays these effects as a dot plot with vertical lines extending from zero to each effect estimate.

For dynamic effects (when fit contains a_dynamic/b_dynamic), additional options are available to visualize how effects evolve over time.

Examples

# \donttest{
# Fit an AME model
data(YX_nrm)
fit <- ame(YX_nrm$Y, Xdyad = YX_nrm$X,
           nscan = 100, burn = 10, odens = 1, verbose = FALSE)

# Visualize sender effects
ab_plot(fit, effect = "sender")

# Visualize receiver effects without sorting
ab_plot(fit, effect = "receiver", sorted = FALSE)
# }