plot_diffnet {netdiffuseR}

Plot the diffusion process

Description

Creates a colored network plot showing the structure of the graph through time (one network plot for each time period) and the set of adopter and non-adopters in the network.

Usage

plot_diffnet(...)

## S3 method for class 'diffnet'
plot_diffnet(graph, ...)

## Default S3 method:
plot_diffnet(
  graph,
  cumadopt,
  slices = NULL,
  vertex.color = c("white", "tomato", "steelblue"),
  vertex.shape = c("square", "circle", "circle"),
  vertex.size = "degree",
  mfrow.par = NULL,
  main = c("Network in period %s", "Diffusion Network"),
  legend.args = list(),
  minmax.relative.size = getOption("diffnet.minmax.relative.size", c(0.01, 0.04)),
  background = NULL,
  ...
)

Arguments

...

Further arguments to be passed to plot.igraph.

graph

A dynamic graph (see netdiffuseR-graphs).

cumadopt

n\times T matrix.

slices

Integer vector. Indicates what slices to plot. By default all are plotted.

vertex.color

A character vector of size 3 with colors names.

vertex.shape

A character vector of size 3 with shape names.

vertex.size

Either a numeric scalar or vector of size n, or any of the following values: "indegree", "degree", or "outdegree" (see details).

mfrow.par

Vector of size 2 with number of rows and columns to be passed to par.

main

Character scalar. A title template to be passed to sprintf.

legend.args

List of arguments to be passed to legend.

minmax.relative.size

Passed to rescale_vertex_igraph.

background

Either a function to be called before plotting each slice, a color to specify the backgroupd color, or NULL (in which case nothing is done).

Details

Plotting is done via the function plot.igraph.

When vertex.size is either of "degree", "indegree", or "outdegree", vertex.size will be replace with dgr(.,cmode = ) so that the vertex size reflects the desired degree.

The argument minmax.relative.size is passed to rescale_vertex_igraph which adjusts vertex.size so that the largest and smallest vertices have a relative size of minmax.relative.size[2] and minmax.relative.size[1] respectively with respect to the x-axis.

Plotting is done via the function plot.igraph.

In order to center the attention on the diffusion process itself, the positions of each vertex are computed only once by aggregating the networks through time, this is, instead of computing the layout for each time t, the function creates a new graph accumulating links through time.

The mfrow.par sets how to arrange the plots on the device. If T=5 and mfrow.par=c(2,3), the first three networks will be in the top of the device and the last two in the bottom.

The argument vertex.color contains the colors of non-adopters, new-adopters, and adopters respectively. The new adopters (default color "tomato") have a different color that the adopters when the graph is at their time of adoption, hence, when the graph been plotted is in t=2 and toa=2 the vertex will be plotted in red.

legend.args has the following default parameter:

x	"bottom"
legend	c("Non adopters", "New adopters","Adopters")
pch	sapply(vertex.shape, switch, circle = 21, square = 22, 21)
bty	"n"
horiz	TRUE

Value

Calculated coordinates for the grouped graph (invisible).

Author(s)

George G. Vega Yon

See Also

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

plot_diffnet(graph, adopt$cumadopt)