After calculating infectiousness and susceptibility of each individual on the
network, it creates an nlevels
by nlevels
matrix indicating the
number of individuals that lie within each cell, and draws a heatmap.
plot_infectsuscep(
graph,
toa,
t0 = NULL,
normalize = TRUE,
K = 1L,
r = 0.5,
expdiscount = FALSE,
bins = 20,
nlevels = round(bins/2),
h = NULL,
logscale = TRUE,
main = "Distribution of Infectiousness and\nSusceptibility",
xlab = "Infectiousness of ego",
ylab = "Susceptibility of ego",
sub = ifelse(logscale, "(in log-scale)", NA),
color.palette = function(n) viridisLite::viridis(n),
include.grid = TRUE,
exclude.zeros = FALSE,
valued = getOption("diffnet.valued", FALSE),
...
)
A list with three elements:
A numeric vector of size \(n\) with infectiousness levels
A numeric vector of size \(n\) with susceptibility levels
A list containing the class marks and counts used to draw the
plot via filled.contour
(see grid_distribution
)
A logical vector with TRUE
when the case was included in
the plot. (this is relevant whenever logscale=TRUE
)
A dynamic graph (see netdiffuseR-graphs
).
Integer vector of length \(n\) with the times of adoption.
Integer scalar. See toa_mat
.
Logical scalar. Passed to infection/susceptibility.
Integer scalar. Passed to infection/susceptibility.
Numeric scalar. Passed to infection/susceptibility.
Logical scalar. Passed to infection/susceptibility.
Integer scalar. Size of the grid (\(n\)).
Integer scalar. Number of levels to plot (see filled.contour
).
Numeric vector of length 2. Passed to kde2d
in the MASS package.
Logical scalar. When TRUE the axis of the plot will be presented in log-scale.
Character scalar. Title of the graph.
Character scalar. Title of the x-axis.
Character scalar. Title of the y-axis.
Character scalar. Subtitle of the graph.
a color palette function to be used to assign colors in the plot (see filled.contour
).
Logical scalar. When TRUE, the grid of the graph is drawn.
Logical scalar. When TRUE, observations with zero values
Logical scalar. When FALSE non-zero values in the adjmat are set to one.
in infect or suscept are excluded from the graph. This is done explicitly when logscale=TRUE
.
Additional parameters to be passed to filled.contour.
George G. Vega Yon
This plotting function was inspired by Aral, S., & Walker, D. (2012).
By default the function will try to apply a kernel smooth function via
kde2d
. If not possible (because not enought data points), then
the user should try changing the parameter h
or set it equal to zero.
toa
is passed to infection/susceptibility
.
Aral, S., & Walker, D. (2012). "Identifying Influential and Susceptible Members of Social Networks". Science, 337(6092), 337–341. tools:::Rd_expr_doi("10.1126/science.1215842")
Infectiousness and susceptibility are computed via infection
and
susceptibility
.
Other visualizations:
dgr()
,
diffusionMap()
,
drawColorKey()
,
grid_distribution()
,
hazard_rate()
,
plot_adopters()
,
plot_diffnet2()
,
plot_diffnet()
,
plot_threshold()
,
rescale_vertex_igraph()
# Generating a random graph -------------------------------------------------
set.seed(1234)
n <- 100
nper <- 20
graph <- rgraph_er(n,nper, p=.2, undirected = FALSE)
toa <- sample(1:(1+nper-1), n, TRUE)
# Visualizing distribution of suscep/infect
out <- plot_infectsuscep(graph, toa, K=3, logscale = FALSE)
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