PAFit (version 0.9.5)

plot.PAFit_result: Plotting 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 or the estimated attachment exponent ($\alpha$ when assuming $A_k = k^\alpha$).

Usage

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

Arguments

x

An object of class "PAFit_result", containing the result

net_stat

An object of class "PAFit_data", 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")
net        <- GenerateNet(N = 50 , m = 1 , mode = 1 , alpha = 1 , shape = 10 , rate = 10)
net_stats  <- GetStatistics(net$graph)
result     <- PAFit(net_stats)
#plot A
plot(result , net_stats , plot = "A")
#plot f
plot(result , net_stats , plot = "f")
#plot true_f
plot(result , net_stats , net$fitness, plot = "true_f")