Learn R Programming

netdiffuseR (version 1.17.0)

plot_adopters: 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("lightblue", "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.

See Also

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

Examples

Run this code
# 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)

Run the code above in your browser using DataLab