plot.PA_result: Plotting the estimated attachment function

In PAFit: Generative Mechanism Estimation in Temporal Complex Networks

Description

This function plots the estimated attachment function from the corrected Newman's method or the Jeong's method. Its also plots additional information such as the estimated attachment exponenent (α when assuming A_k = k^α).

Usage

## S3 method for class 'PA_result'
plot(x, 
     net_stat    = NULL,
     plot_bin    = TRUE   ,
     high_deg    = 1      ,  
     line        = FALSE  , 
     col_point   = "black",
     shade_point = 0.5    , 
     pch         = 16     ,
     max_A       = NULL   , 
     min_A       = NULL   , 
     label_x     = NULL   , 
     label_y     = NULL   ,
     ...)

Arguments

x	An object of class PA_result, containing the estimated attachment function and the estimated attachment exponenet from either Newman or Jeong functions.
net_stat	An object of class PA_data, containing the summerized statistics. This object is created from the function get_statistics.
plot_bin	Logical. If TRUE then only the center of each bin is plotted. Default is TRUE.
high_deg	Integer. Specifies the starting degree from which A_k is plotted. If this parameter is specified, the estimated attachment function is plotted from k = high_deg
line	Logical. Indicates whether to plot the line fitted from the log-linear model or not. Default value is FALSE.
col_point	String. The name of the color of the points. Default value is "black".
shade_point	Numeric. Value between 0 and 1. This is the transparency level of the points. Default value is 0.5.
pch	Numeric. The plot symbol. Default value is 16.
max_A	Numeric. Specify the maximum of the horizontal axis.
min_A	Numeric. Specify the minimum of the horizontal axis.
label_x	String. The label of x-axis. If NULL, then "Degree k" is used.
label_y	String. The label of y-axis. If NULL, then "Attachment function" is used.
...

Value

Outputs the desired plot.

Author(s)

Thong Pham [e-mail]

Examples

  library("PAFit")
  net        <- generate_net(N = 1000 , m = 1 , mode = 1 , alpha = 1 , s = 0)
  net_stats  <- get_statistics(net)
  result     <- Newman(net, net_stats)
  # true function
  true_A     <- result$center_k
  # plot the estimated attachment function
  plot(result , net_stats)
  lines(result$center_k, true_A, col = "red") # true attachment function
  legend("topleft" , legend = "True function" , col = "red" , lty = 1 , bty = "n")

PAFit documentation built on Jan. 18, 2022, 1:10 a.m.