PAFit (version 0.8.7)

plot.PAFit: A function to plot the estimated attachment function and node fitness

Description

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

Usage

"plot"(x,data,true_f = NULL, plot = c("A","f","true_f"), plot_bin = TRUE, line = FALSE, confidence = TRUE, high_deg = NULL,  shade_point = 0.5, shade_interval = 0.5,  max_A = NULL,  min_A = NULL, f_min = NULL,  f_max = NULL, plot_true_degree = FALSE,  label_x = NULL, label_y = NULL, col_interval = "lightsteelblue", col_point = "black",...)

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 $\eta$ versus true $\eta$ is produced (after a suitable rescaling of the estimated $f$).

plot

String. Indicates which plot is produced.if $"A"$ then PA function is plotted. If $"f"$ then estimated fitness is plotted. If $"true_f"$ then estimated fitness and true fitness are plotted together (require supplement of true fitness). 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 $eta_i$ or not. If confidence == TRUE, a 2-sigma confidence interval will be plotted at each $A_k$ and $eta_i$.

high_deg

Integer. If this parameter is specified, only nodes whose number of edges acquired is not less than $high_deg$ is plotted.

shade_point

Numeric. Value between 0 and 1. This is the transparency level of the points. Default value is $0.5$.

shade_interval

Numeric. Value between 0 and 1. This is the transparency level of the confidence intervals. Default value is $0.5$.

max_A

Numeric. Specify the maximum of the axis of PA.

min_A

Numeric. Specify the minimum of the axis of PA.

f_min

Numeric. Specify the minimum of the axis of fitness.

f_max

Numeric. Specify the maximum of the axis of fitness.

plot_true_degree

Logical. The degree of each node is plotted or not.

label_x

String. The label of x-axis.

label_y

String. The label of y-axis.

col_interval

String. The name of the color of the confidence intervals. Default value is $"lightsteelblue"$.

col_point

String. The name of the color of the points. Default value is $"black"$.

...

Value

Outputs the desired plot.

References

1. Pham, T., Sheridan, P. & Shimodaira, H. (2016). Nonparametric Estimation of the Preferential Attachment Function in Complex Networks: Evidence of Deviations from Log Linearity, Proceedings of ECCS 2014, 141-153 (Springer International Publishing) (http://dx.doi.org/10.1007/978-3-319-29228-1_13).

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).

3. Pham, T., Sheridan, P. & Shimodaira, H. (2016). Joint Estimation of Preferential Attachment and Node Fitness in Growing Complex Networks. Scientific Reports 6, Article number: 32558. doi:10.1038/srep32558 (www.nature.com/articles/srep32558).

Examples

library("PAFit")
data   <- GenerateNet(N = 100,m = 1,mode = 1, alpha = 1, shape = 5, rate = 5)
stats  <- GetStatistics(data$graph,deg_thresh = 1, Binning = TRUE, G = 50)
result <- PAFit(stats,stop_cond = 10^-3)
#plot A
plot(result,stats,plot = "A")
#plot f
plot(result,stats,plot = "f")
#plot true_f
plot(result,stats,data$fitness, plot = "true_f")