Nothing
only_F_estimate <- function(net_object,
net_stat = get_statistics(net_object),
p = 0.75 ,
stop_cond = 10^-8 ,
model_A = "Linear" ,
...) {
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");
mode_f <- "Log_linear"
if (model_A == "Linear")
alpha_start <- 1
else if (model_A == "Constant")
alpha_start <- 0
net_type <- net_stat$net_type
data_cv <- .CreateDataCV(net_object, deg_thresh = net_stat$deg_thresh, p = p)
cv_result <- .OnlyF_CV(data_cv,
stop_cond = stop_cond,
alpha = alpha_start,
...)
f_vector <- rep(1,length(net_stat$f_position))
#print(paste0("length f_vector before: ",length(f_vector)))
names(f_vector) <- net_stat$f_position
#print(length(cv_result$estimated_fitness))
f_vector[names(cv_result$estimated_fitness)] <- cv_result$estimated_fitness
#print(paste0("length f_vector after: ",length(f_vector)))
result <- PAFit(net_stat,
only_f = TRUE ,
mode_f = mode_f ,
start_f = f_vector ,
s = cv_result$s_optimal ,
alpha_start = alpha_start ,
stop_cond = stop_cond ,
...)
#stop("reach here")
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"
class(combined_result) <- "Full_PAFit_result"
return(combined_result)
}
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