netdiffuseR (version 1.22.6)

plot_threshold: Threshold levels through time

Description

Draws a graph where the coordinates are given by time of adoption, x-axis, and threshold level, y-axis.

Usage

plot_threshold(graph, expo, ...)
# S3 method for diffnet
plot_threshold(graph, expo, ...)
# S3 method for array
plot_threshold(graph, expo, ...)
# S3 method for default
plot_threshold(
  graph,
  expo,
  toa,
  include_censored = FALSE,
  t0 = min(toa, na.rm = TRUE),
  attrs = NULL,
  undirected = getOption("diffnet.undirected"),
  no.contemporary = TRUE,
  main = "Time of Adoption by\nNetwork Threshold",
  xlab = "Time",
  ylab = "Threshold",
  vertex.size = "degree",
  vertex.color = NULL,
  vertex.label = "",
  vertex.label.pos = NULL,
  vertex.label.cex = 1,
  vertex.label.adj = c(0.5, 0.5),
  vertex.label.color = NULL,
  vertex.sides = 40L,
  vertex.rot = 0,
  edge.width = 2,
  edge.color = NULL,
  arrow.width = NULL,
  arrow.length = NULL,
  arrow.color = NULL,
  include.grid = FALSE,
  vertex.frame.color = NULL,
  bty = "n",
  jitter.factor = c(1, 1),
  jitter.amount = c(0.25, 0.025),
  xlim = NULL,
  ylim = NULL,
  edge.curved = NULL,
  background = NULL,
  ...
)

Value

Invisible. A data frame with the calculated coordinates, including: `toa`, `threshold`, and `jit` (a jittered version of `toa`).

Arguments

graph

A dynamic graph (see netdiffuseR-graphs).

expo

\(n\times T\) matrix. Esposure to the innovation obtained from exposure

...

Additional arguments passed to plot.

toa

Integer vector of length \(n\) with the times of adoption.

include_censored

Logical scalar. Passed to threshold.

t0

Integer scalar. Passed to threshold.

attrs

Passed to exposure (via threshold).

undirected

Logical scalar. When TRUE only the lower triangle of the adjacency matrix will considered (faster).

no.contemporary

Logical scalar. When TRUE, edges for vertices with the same toa won't be plotted.

main

Character scalar. Title of the plot.

xlab

Character scalar. x-axis label.

ylab

Character scalar. y-axis label.

vertex.size

Numeric vector of size \(n\). Relative size of the vertices.

vertex.color

Either a vector of size \(n\) or a scalar indicating colors of the vertices.

vertex.label

Character vector of size \(n\). Labels of the vertices.

vertex.label.pos

Integer value to be passed to text via pos.

vertex.label.cex

Either a numeric scalar or vector of size \(n\). Passed to text.

vertex.label.adj

Passed to text.

vertex.label.color

Passed to text.

vertex.sides

Either a vector of size \(n\) or a scalar indicating the number of sides of each vertex (see details).

vertex.rot

Either a vector of size \(n\) or a scalar indicating the rotation in radians of each vertex (see details).

edge.width

Numeric. Width of the edges.

edge.color

Character. Color of the edges.

arrow.width

Numeric value to be passed to arrows.

arrow.length

Numeric value to be passed to arrows.

arrow.color

Color.

include.grid

Logical. When TRUE, the grid of the graph is drawn.

vertex.frame.color

Either a vector of size \(n\) or a scalar indicating colors of vertices' borders.

bty

See par.

jitter.factor

Numeric vector of size 2 (for x and y) passed to jitter.

jitter.amount

Numeric vector of size 2 (for x and y) passed to jitter.

xlim

Passed to plot.

ylim

Passed to plot.

edge.curved

Logical scalar. When curved, generates curved edges.

background

TBD

Author

George G. Vega Yon

Details

When vertex.label=NULL the function uses vertices ids as labels. By default vertex.label="" plots no labels.

Vertices are drawn using an internal function for generating polygons. Polygons are inscribed in a circle of radius vertex.size, and can be rotated using vertex.rot. The number of sides of each polygon is set via vertex.sides.

See Also

Use threshold to retrieve the corresponding threshold obtained returned by exposure.

Other visualizations: dgr(), diffusionMap(), drawColorKey(), grid_distribution(), hazard_rate(), plot_adopters(), plot_diffnet2(), plot_diffnet(), plot_infectsuscep(), rescale_vertex_igraph()

Examples

# Generating a random graph
set.seed(1234)
n <- 6
nper <- 5
graph <- rgraph_er(n,nper, p=.3, undirected = FALSE)
toa <- sample(2000:(2000+nper-1), n, TRUE)
adopt <- toa_mat(toa)

# Computing exposure
expos <- exposure(graph, adopt$cumadopt)

plot_threshold(graph, expos, toa)

# Calculating degree (for sizing the vertices)
plot_threshold(graph, expos, toa, vertex.size = "indegree")