plot_adopters: Visualize adopters and cumulative adopters

In netdiffuseR: Analysis of Diffusion and Contagion Processes on Networks

Visualize adopters and cumulative adopters

Description

Visualize adopters and cumulative adopters

Usage

plot_adopters(
  obj,
  freq = FALSE,
  what = c("adopt", "cumadopt"),
  add = FALSE,
  include.legend = TRUE,
  include.grid = TRUE,
  pch = c(21, 24),
  type = c("b", "b"),
  ylim = if (!freq) c(0, 1) else NULL,
  lty = c(1, 1),
  col = c("black", "black"),
  bg = c("tomato", "gray"),
  xlab = "Time",
  ylab = ifelse(freq, "Frequency", "Proportion"),
  main = "Adopters and Cumulative Adopters",
  ...
)

Arguments

obj	Either a diffnet object or a cumulative a doption matrix.
freq	Logical scalar. When TRUE frequencies are plotted instead of proportions.
what	Character vector of length 2. What to plot.
add	Logical scalar. When TRUE lines and dots are added to the current graph.
include.legend	Logical scalar. When TRUE a legend of the graph is plotted.
include.grid	Logical scalar. When TRUE, the grid of the graph is drawn
pch	Integer vector of length 2. See matplot.
type	Character vector of length 2. See matplot.
ylim	Numeric vector of length 2. Sets the plotting limit for the y-axis.
lty	Numeric vector of length 2. See matplot.
col	Character vector of length 2. See matplot.
bg	Character vector of length 2. See matplot.
xlab	Character scalar. Name of the x-axis.
ylab	Character scalar. Name of the y-axis.
main	Character scalar. Title of the plot
...	Further arguments passed to matplot.

Value

A matrix as described in cumulative_adopt_count.

Author(s)

George G. Vega Yon

See Also

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

Examples

# Generating a random diffnet -----------------------------------------------
set.seed(821)
diffnet <- rdiffnet(100, 5, seed.graph="small-world", seed.nodes="central")

plot_adopters(diffnet)

# Alternatively, we can use a TOA Matrix
toa <- sample(c(NA, 2010L,2015L), 20, TRUE)
mat <- toa_mat(toa)
plot_adopters(mat$cumadopt)

netdiffuseR documentation built on Aug. 30, 2023, 5:07 p.m.