Nothing
only_A_estimate <- function(net_object ,
net_stat = get_statistics(net_object),
p = 0.75 ,
stop_cond = 10^-8 ,
mode_reg_A = 0 ,
MLE = FALSE ,
...) {
if (!is(net_object,"PAFit_net"))
stop("net_object should be of PAFit_net class.")
if (!is(net_stat,"PAFit_data"))
stop("Please input a proper net summary of class PAFit_data");
# quick check
non_zero_theta <- which(net_stat$sum_m_k > 0)
num_nonzero <- length(non_zero_theta)
if (num_nonzero == 1) {
# only one non-zero bin
stop(paste0("Error: There is only one bin that has a non-zero number of new edges (bin ",which(net_stat$sum_m_k > 0),"). To estimate the PA function, we need at least two bins with non-zero number of new edges."))
}
if (num_nonzero == 0) {
# no non-zero bin
stop(paste0("Error: There is no bin that has a non-zero number of new edges. To estimate the PA function, we need at least two bins with non-zero number of new edges."))
}
if (MLE == FALSE) {
net_type <- net_stat$net_type
data_cv <- .CreateDataCV_onlyA(net_object, deg_thresh = net_stat$deg_thresh, p = p)
cv_result <- .OnlyA_CV(data_cv, stop_cond = stop_cond * 10, mode_reg_A = mode_reg_A,...)
# find a rough estimate of the attachment function based on the model Ak = k^alpha
result_temp <- PAFit(net_stat,
mode_f = "Log_linear" ,
only_PA = TRUE ,
alpha_start = cv_result$alpha_optimal ,
stop_cond = stop_cond * 10 , # loose convergence condition
...)
# feed the estimated attachment function for a warm-start re-run with nonparametric attachment function
result <- PAFit(net_stat,
r = cv_result$r_optimal ,
mode_f = "Linear_PA" ,
only_PA = TRUE ,
#alpha_start = result_temp$alpha ,
alpha_start = cv_result$alpha_optimal ,
stop_cond = stop_cond ,
mode_reg_A = mode_reg_A ,
...)
} else {
result <- PAFit(net_stat,
r = 0,
mode_f = "Linear_PA" ,
only_PA = TRUE ,
stop_cond = stop_cond ,
...)
data_cv <- NULL
cv_result <- NULL
}
PA <- result$A
fit <- result$f
small_t <- dim(net_stat$node_degree)[1]
contrib_PA_array <- rep(0,small_t)
contrib_fit_array <- rep(0,small_t)
name_node <- colnames(net_stat$node_degree)
for (i in 1:small_t) {
presence <- net_stat$node_degree[i,] > 0
# sampling the node based on the product of PA and fitness
pa_value <- PA[net_stat$node_degree[i,presence] + 1]
pa_value[is.na(pa_value)] <- PA[length(PA)]
#print(length(pa_value))
fitness_value <- fit[name_node[presence]]
#print(length(fitness_value))
sampling_prob <- fitness_value * pa_value / sum(fitness_value * pa_value)
mean_log_PA <- mean(sampling_prob * log(pa_value), na.rm = TRUE)
var_log_PA <- mean(sampling_prob* (log(pa_value) - mean_log_PA)^2, na.rm = TRUE)
mean_log_fit <- mean(sampling_prob * log(fitness_value) , na.rm = TRUE)
var_log_fit <- mean(sampling_prob* (log(fitness_value) - mean_log_fit)^2 , na.rm = TRUE)
contrib_PA <- var_log_PA
contrib_fit <- var_log_fit
contrib_PA_array[i] <- contrib_PA
contrib_fit_array[i] <- contrib_fit
}
mean_PA_contrib <- sqrt(mean(contrib_PA_array, na.rm = TRUE))
mean_fit_contrib <- sqrt(mean(contrib_fit_array, na.rm = TRUE))
contribution <- list(PA_contribution = sqrt(contrib_PA_array),
fit_contribution = sqrt(contrib_fit_array),
mean_PA_contrib = mean_PA_contrib,
mean_fit_contrib = mean_fit_contrib)
combined_result <- list(cv_data = data_cv, cv_result = cv_result,
estimate_result = result, contribution = contribution)
class(combined_result) <- "Full_PAFit_result"
return(combined_result)
}
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