plot.snowboot: Plot Degree Distribution Estimates

In snowboot: Bootstrap Methods for Network Inference

Description

Plot LSMI-based point estimates of probabilities of node degrees, \hat{f}(k), and of mean degree, \hat{μ}, where k = 0, 1, … are the degrees. The point estimates are supplemented with box-and-whisker plots of bootstrapped values (if the input is a boot_dd output) or element-wise bootstrap confidence intervals (if the input is a boot_ci output). See \insertCitechen_etal_2018_snowboot;textualsnowboot.

Usage

## S3 method for class 'snowboot'
plot(
  x,
  k = NULL,
  plotmu = TRUE,
  plotlegend = TRUE,
  col0 = "gray50",
  lwd0 = 1,
  colpt = "royalblue3",
  lwdpt = 2,
  pchpt = 4,
  coli = "palegreen3",
  colibg = "palegreen",
  length = 0.1,
  boxwex = 0.4,
  legendargs = list(x = "topright", cex = 0.9, bty = "n"),
  las = 1,
  ...
)

Arguments

x	output of lsmi_dd, boot_dd, or boot_ci.
k	an integer vector with degrees to plot. By default, all degrees represented in x are plotted.
plotmu	logical value indicating whether to plot the results for mean degree (default is TRUE).
plotlegend	logical value indicating whether to plot a legend (default is TRUE).
col0	color to plot horizontal zero-line at f(k) = 0. Use NA for no plotting.
lwd0	width of the horizontal zero-line at f(k) = 0.
colpt	color for plotting point estimates.
lwdpt	line width for plotting point estimates.
pchpt	point type for plotting point estimates (see argument pch in points).
coli	color for plotting lines or borders of box-plots for bootstrap estimates.
colibg	background color, if plotting boxplots of bootstrapped estimates (see argument border in boxplot).
length	length of arrows, if plotting bootstrap confidence intervals (see argument length in arrows).
boxwex	argument of boxplot function.
legendargs	additional arguments for plotting the legend (see arguments in legend).
las	argument of plot function.
...	additional arguments to pass to the plot function.

References

\insertAllCited

Examples

net <- artificial_networks[[1]]
x <- lsmi_dd(net = net, n.wave = 2, n.seed = 40)
plot(x)

x2 <- boot_dd(x)
plot(x2, k = c(1:10))

x3 <- boot_ci(x2, prob = 0.99)
plot(x3, k = c(1:10))

snowboot documentation built on April 26, 2020, 1:05 a.m.