# 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

  1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 ## 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.

\insertAllCited

## Examples

 1 2 3 4 5 6 7 8 9 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.