R Under development (unstable) (2024-03-25 r86192 ucrt) -- "Unsuffered Consequences"
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> if (FALSE) {
+ rm(list = ls())
+ set.seed(1)
+ library("PAFit")
+ # size of initial network = 100
+ # number of new nodes at each time-step = 100
+ # Ak = k; inverse variance of the distribution of node fitnesse = 5
+ 
+ #alpha <- rep(0,100)
+ s <- rep(0,50)
+ alpha <- rep(0,50)
+ for (j in 1:50) {
+ print(paste0("j:",j))
+ net        <- generate_net(N        = 1000 , m             = 50 , 
+                           num_seed = 200  , multiple_node = 50, alpha = -1,
+                           s        = 5)
+ net_stats  <- get_statistics(net,deg_threshold = 0)
+ result_big <- joint_estimate(net,net_stats)
+ s[j] <- result_big$estimate_result$shape
+ alpha[j] <- result_big$estimate_result$alpha
+ #data_cv <- .CreateDataCV(net, g = 50, deg_thresh = 0,
+ #                          p = 0.75)
+ 
+ #cv_result <- PAFit(data_cv$stats,s = 5,mode_f = "Log_linear")
+ #alpha[j] <- cv_result$alpha
+ }
+ }
> # Joint estimation of attachment function Ak and node fitness
> #result     <- joint_estimate(net, net_stats)
> 
> 
> #data_cv <- .CreateDataCV(net, g = 50, deg_thresh = 0,
> #                          p = 0.75)
> 
> #cv_result <- PAFit(data_cv$stats, r = 0.1, s = 5)
> 
> # name_z_j <- intersect(names(data_cv$stats$z_j), as.character(as.numeric(data_cv$stats$node_before_final)))
> # z_j_before_final <- data_cv$stats$z_j[name_z_j]
> # no_edge <- names(z_j_before_final[z_j_before_final == 0])
> # cv_result$f[no_edge]
> # cv_result$var_f[no_edge]
> # plot(cv_result,data_cv$stats)
> # cv_result
> 
> 
> 
> 
> #result_only_F <- only_F_estimate(net,net_stats)
> 
> # plot(result_big,net_stats)
> # plot(result_big, net_stats, true = net$fitness, plot = "true_f")
> # 
> 
> proc.time()
   user  system elapsed 
   0.15    0.04    0.14