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Last data update: 2014.03.03

R: A function to plot the estimated attachment function and node...

plot.PAFit

R Documentation

A function to plot the estimated attachment function and node fitness

Description

This function plots the estimated attachment function A_k and node fitness f_i, together with additional information such as their confidence intervals.

Usage

## S3 method for class 'PAFit'
plot(x, data, true_f = NULL, plot = c("A","f","true_f"),
plot_bin = TRUE, line = TRUE, confidence = TRUE, high_deg = NULL, shade = 0.5, ...)

Arguments

`x`	An object of class "PAFit", containing the result
`data`	An object of class "PAFitData", containing the summerized statistics.
`true_f`	Vector. Optional parameter for the true value of node fitnesses (only available in simulated datasets). If this parameter is specified and plot == "true_f", a plot of estimated f versus true f is produced (after a suitable rescaling of the estimated f).
`plot`	String. Indicates which plot is produced. Default value is "A".
`plot_bin`	Logical. If TRUE then only the center of each bin is plotted. Default is TRUE.
`line`	Logical. Indicates whether to plot the line fitted from the log-linear model or not. Default value is TRUE.
`confidence`	Logical. Indicates whether to plot the confidence intervals of A_k and f_i or not. If confidence == TRUE, a 2-sigma confidence interval will be plotted at each A_k and f_i.
`high_deg`	Integer. If this parameter is specified, only nodes whose number of edges acquired is not less than high_deg is plotted.
`shade`	Numeric. Value between 0 and 1. This is the transparency level of the confidence intervals. Default value is 0.5.
`...`

Value

Outputs the desired plot.

Author(s)

Thong Pham thongpham@thongpham.net

References

1. Pham, T. and Sheridan, P. and Shimodaira, H. (2015). Nonparametric estimation of the preferential attachment function in complex networks: evidence of deviations from log linearity, in press. Proceedings of ECCS 2014: European Conference on Complex Systems.

2. Pham T, Sheridan P, Shimodaira H (2015) PAFit: A Statistical Method for Measuring Preferential Attachment in Temporal Complex Networks. PLoS ONE 10(9): e0137796. doi:10.1371/journal.pone.0137796 (http://dx.doi.org/10.1371/journal.pone.0137796)

Examples

library("PAFit")
data   <- GenerateNet(N = 1000,m = 1,mode = 1, alpha = 1, shape = 5, rate = 5)
stats  <- GetStatistics(data$graph)
result <- PAFit(stats)
#plot A
plot(result,stats,plot = "A")
#plot f
plot(result,stats,plot = "f")
#plot true_f
plot(result,stats,data$fitness, plot = "true_f")

Results


R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(PAFit)
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/PAFit/plot.PAFit.Rd_%03d_medium.png", width=480, height=480)
> ### Name: plot.PAFit
> ### Title: A function to plot the estimated attachment function and node
> ###   fitness
> ### Aliases: plot.PAFit
> 
> ### ** Examples
> 
> library("PAFit")
> data   <- GenerateNet(N = 1000,m = 1,mode = 1, alpha = 1, shape = 5, rate = 5)
> stats  <- GetStatistics(data$graph)
> result <- PAFit(stats)
> #plot A
> plot(result,stats,plot = "A")
> #plot f
> plot(result,stats,plot = "f")
> #plot true_f
> plot(result,stats,data$fitness, plot = "true_f")
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>